publications

Publications by categories in reversed chronological order.

journals
books
chapters
reports
patents
outreach
.txt
.bib

2025

  1. Yin2025a.jpg
    Jin, Y., Pepe, A., Li, J., Gsaxner, C., Chen, Y., and 6 more authors. (2025). Aortic Vessel Tree Segmentation for Cardiovascular Diseases Treatment: Status Quo. ACM Comput. Surv., 57(238):1–35.
  2. Song2025a.png
    Song, X., Yang, P., Zhou, F., Frangi, A. F. , Xiao, X., and 3 more authors. (2025). Knowledge-aware Multisite Adaptive Graph Transformer for Brain Disorder Diagnosis. IEEE Trans Med Imaging., 44(6):2370–2383.
  3. Lekadir2025a.png
    Lekadir, K., Frangi, A. F. , Porras, A. R., Glocker, B., Cintas, C., and 44 more authors. (2025). FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare. BMJ, 388:e081554.
  4. Huang, Y., Chang, A., Dou, H., Tao, X., Zhou, X., and 6 more authors. (2025). Flip Learning: Weakly supervised erase to segment nodules in breast ultrasound. Med Image Anal, 102:103552.
  5. Liu, Q., Lassila, T., Lin, F., MacRaild, M., Patankar, T., and 7 more authors. (2025). Key influencers in an aneurysmal thrombosis model: A sensitivity analysis and validation study. APL Bioeng, 9(1):016107.
  6. Ru, X., Wang, X., Liu, Z., Du, P., Zhao, H., and 5 more authors. (2025). Pose-independent efficient gauge equivariant network for 3D mesh aneurysm segmentation. Neurocomputing., 639:130188.
  7. Gaggion, N., Matheson, B. A., Xia, Y., Bonazzola, R., Ravikumar, N., and 4 more authors. (2025). Multi-view Hybrid Graph Convolutional Network for Volume-to-mesh Reconstruction in Cardiovascular MRI. Med Image Anal, 104:103630.
  8. MacRaild, M., Sarrami-Foroushani, A., Patankar, T., and Frangi, A. F. . (2025). In-Silico Neurosurgery: Toward Safe, Effective and Equitable Flow Diverter Treatment of Intracranial Aneurysms. World Neurosurg, 195:123589.
  9. Mekki2025a.png
    Mekki, Y. M., Luijten, G., Hagert, E., Belkhair, S., Varghese, C., and 10 more authors. (2025). Digital twins for the era of personalized surgery. NPJ Digit Med, 8(1):283.
  10. Chakraborti2025a.png
    Chakraborti, T., Banerji, C. R. S., Marandon, A., Hellon, V., Mitra, R., and 11 more authors. (2025). Personalized uncertainty quantification in artificial intelligence. Nat. Mach. Intell., 7:522–530.
  11. Nan, Y., Zhou, H., Xing, X., Papanastasiou, G., Zhu, L., and 3 more authors. (2025). Revisiting medical image retrieval via knowledge consolidation. Med Image Anal., 102:103553.
  12. Hsu, W.-C., Meuschke, M., Frangi, A. F. , Preim, B., and Lawonn, K. (2025). A Survey of Intracranial Aneurysm Detection and Segmentation. Med Image Anal., 101:103493.
  13. Chew2025a.png
    Chew, E., Burns, S., Abraham, A., Bakhoum, M., Beckman, J., and 19 more authors. (2025). Standardization and Clinical Applications of Retinal Imaging Biomarkers for Cardiovascular Disease: NHLBI Workshop/Roadmap. Nat Rev Cardiol., 22(1):47–63.
  14. Dhesi, S. S., Adusumilli, P., Ravikumar, N., Waduud, M. A., Frood, R., and 12 more authors. (2025). Development and External Validation of [18F]FDG PET-CT-Derived Radiomic Models for Prediction of Abdominal Aortic Aneurysm Growth Rate. Algorithms, 18(2):86.
  15. Dou2025a.png
    Dou, H., Virtanen, S., Ravikumar, N., and Frangi, A. F. . (2025). A Generative Shape Compositional Framework to Synthesize Populations of Virtual Chimeras. IEEE Trans Neural Netw Learn Syst., 36(3):4750–4764.
  16. Lashgari2025a.png
    Lashgari, M., Yang, Z., Bernabeu, M. O., Li, J.-R., and Frangi, A. F. . (2025). SpinDoctor-IVIM: A virtual imaging framework for intravoxel incoherent motion MRI. Med Image Anal., 99:103369.
  17. Samei, E., Abadi, E., Bakic, P., Bliznakova, K., Bosmans, H., and 11 more authors. (2025). Virtual imaging trials in medicine: A brief takeaway of the lessons from the first international summit. Med Phys, 52(3):1950–1959.
  18. Munia, A. A., Abdara, M., Hasan, M., Jalali, M. S., Banerjee, B., and 4 more authors. (2025). Attention-Guided Hierarchical Fusion U-Net for Uncertainty-driven Medical Image Segmentation. Inf. Fusion, 1154:102719.

2024

  1. Liu, Z., Wang, X., Wu, Z., Ju, X., Zhu, Y.-C., and 1 more author. (2024). MRI Joint Super-Resolution and Denoising based on Conditional Stochastic Normalizing Flow. IEEE Trans Artif Intell., 6(6):1472–1487.
  2. Thomson, R. J., Grafton-Clarke, C., Matthews, G., Swoboda, P. P., Swift, A. J., and 4 more authors. (2024). Risk factors for raised left ventricular filling pressure by cardiovascular magnetic resonance: Prognostic insights. ESC Heart Fail, 11(6):4148–4159.
  3. Wong, M. Y. Z., Vargas, J. D., Naderi, H., Sanghvi, M. M., Raisi-Estabragh, Z., and 10 more authors. (2024). Concurrent Left Ventricular Myocardial Diffuse Fibrosis and Left Atrial Dysfunction Strongly Predict Incident Heart Failure. JACC Cardiovasc Imaging, 17(5):560–562.
  4. Abadi, E., Barufaldi, B., Lago, M., Badal, A., Mello-Thoms, C., and 10 more authors. (2024). Towards widespread use of virtual trials in medical imaging innovation and regulatory science. Med Phys., 51(12):9394–9404.
  5. Aksoy, N., Sharoff, S., Baser, S., Ravikumar, N., and Frangi, A. F. . (2024). Beyond images: an integrative multi-modal approach to chest x-ray report generation. Front Radiol., 4:1339612.
  6. Bonazzola, R., Ferrante, E., Ravikumar, N., Xia, Y., Keavney, B., and 3 more authors. (2024). Unsupervised ensemble-based phenotyping enhances gene discoverability in imaging genetics: new associations from left-ventricular morphology. Nature Mach. Intell., 6(3):291–306.
  7. Chen, X., Xia, Y., Dall’Armellina, E., Ravikumar, N., and Frangi, A. F. . (2024). Joint shape/texture representation learning for cardiovascular disease diagnosis from MRI. Eur Heart J - Imag Meth Practice, 2(1):qyae042.
  8. Girach, Z., Sarian, A., Maldonado-García, C., Ravikumar, N., Sergouniotis, P. I., and 3 more authors. (2024). Retinal imaging for the assessment of stroke risk: a systematic review. J Neurol., 271(5):2285–2297.
  9. Harkness, R., Frangi, A. F. , Zucker, K., and Ravikumar, N. (2024). Multi-centre benchmarking of deep learning models for COVID-19 detection in chest x-rays. Front. Radiol., 4:1386906.
  10. Liu, X., Wu, Z., Wang, X., Liu, Q., Pozo, J. M., and 1 more author. (2024). Joint magnetic resonance imaging artifacts and noise reduction on discrete shape space of images. Pattern Recognit., 153:110495.
  11. MacRaild, M., Sarrami-Foroushani, A., Lassila, T., and Frangi, A. F. . (2024). Accelerated simulation methodologies for computational vascular flow modelling. J R Soc Interface, 21:20230565.
  12. MacRaild, M., Sarrami-Foroushani, A., Lassila, T., and Frangi, A. F. . (2024). Reduced order modelling of intracranial aneurysm flow using proper orthogonal decomposition and neural networks. Int J Numer Meth Biomed Eng., 40(10):e3848.
  13. Mou, L., Lin, J., Zhao, Y., Liu, Y., Ma, S., and 5 more authors. (2024). COSTA: A Multi-center TOF-MRA Dataset and A Style Self-Consistency Network for Cerebrovascular Segmentation. IEEE Trans Med Imaging., 43(12):4442–4456.
  14. Wu, K., Xia, Y., Ravikumar, N., and Frangi, A. F. . (2024). Compressed Sensing using a Deep Adaptive Perceptual Generative Adversarial Network for MRI Reconstruction from Undersampled K-space Data. Biomed Signal Process Control., 96:106560.
  15. Zhang, L., Bronik, K., Piechnik, S. K., Lima, J. A. C., Neubauer, S., and 2 more authors. (2024). Automatic Plane Pose Estimation for Cardiac Left Ventricle Coverage Estimation via Deep Adversarial Regression Network. IEEE Trans Artif Intell., 5(4):4738–4752.
  16. Zhang, S., Fang, Y., Nan, Y., Wang, S., Ding, W., and 5 more authors. (2024). Fuzzy Attention-based Border Rendering Orthogonal Network for Lung Organ Segmentation.. IEEE Trans Fuzzy Syst., 32(10):5462–5476.
  17. Zhao, Y., Hao, J., Kwapong, W., Shen, T., Fu, H., and 10 more authors. (2024). Early Detection of Dementia through Retinal Imaging and Trustworthy AI. Nature Digit Med., 7(1):294.
  18. Alzaid, A., Lineham, B., Dogramadzi, S., Pandit, H., Frangi, A. F. , and 1 more author. (2024). Hip Implant Segmentation and Gruen Landmarks Detection. IEEE J Biomed Health Inform, 28(1):333–342.
  19. Xie, J., Yi, Q., Wu, Y., Zheng, Y., Liu, Y., and 11 more authors. (2024). Deep segmentation of OCTA for evaluation and association of changes of retinal microvasculature with Alzheimer’s disease and mild cognitive impairment. Br J Ophthalmol., 108(3):432–439.
  20. Dou, H., Ravikumar, N., and Frangi, A. F. . (2024). Method and Apparatus for Controlled Generation of Virtual Anatomical Population Models. Patent Application PCT/GB2024/051608. World Intellectual Property Organization.

2023

  1. Abdar, M., Fahami, M. A., Rundo, L., Radeva, P., Frangi, A. F. , and 5 more authors. (2023). Hercules: Deep Hierarchical Attentive Multi-Level Fusion Model with Uncertainty Quantification for Medical Image Classification. IEEE Trans Industr Inform., 19(1):274–285.
  2. Aviyente, S., Frangi, A. F. , Meijering, E., Muñoz-Barrutia, A., Liebling, M., and 4 more authors. (2023). From Nano to Macro: An Overview of the IEEE Bio Image and Signal Processing Technical Committee. IEEE Signal Process Mag., 40(4):61–71.
  3. Elhaminia, B., Gilbert, A., Lilley, J., Abdar, M., Frangi, A. F. , and 3 more authors. (2023). Toxicity Prediction in Pelvic Radiotherapy Using Multiple Instance Learning and Cascaded Attention Layers.. IEEE J Biomed Health Inform., 27(4):1958-.1966.
  4. Farzi, M., Coveney, S., Afzali, M., Zdora, M.-C., Lygate, C. A., and 5 more authors. (2023). Measuring Cardiomyocyte Cellular Characteristics in Cardiac Hypertrophy using Diffusion-Weighted MRI. Magn Res Med., 90(5):2144–2157.
  5. Hartung, T., Deng, J., Mall, R., Frangi, A. F. , Emmert-Streib, F., and 1 more author. (2023). Editorial: Insights in AI: Medicine and public health 2022. Front Artif Intell., 2:1166426.
  6. Hu, H., Pan, N., and Frangi, A. F. . (2023). Fully Automatic initialization and segmentation of left and right ventricles for large-scale cardiac MRI using a deeply supervised network and 3D-ASM. Comput Methods Programs Biomed, 240:107679.
  7. Kassab-Bachi, A., Ravikumar, N., Wilcox, R. K., Frangi, A. F. , and Taylor, Z. A. (2023). Contribution of shape features to intradiscal pressure and facets contact pressure in L4/L5 FSUs: An in-silico study. Ann Biomed Eng., 51(1):174–78.
  8. Lin, F., Xia, Y., Song, S., Ravikumar, N., and Frangi, A. F. . (2023). High-Throughput 3DRA Segmentation of Brain Vasculature and Aneurysms using Deep Learning. Comput Methods Programs Biomed, 230:107355.
  9. Liu, Q., Sarrami-Foroushani, A., Wang, Y., MacRaild, M., Kelly, C., and 8 more authors. (2023). Hemodynamics of thrombus formation in intracranial aneurysms: An in silico observational study. APL Bioeng, 7(3):036102.
  10. Luo, M., Yang, X., Wang, H., Dou, H., Hu, X., and 9 more authors. (2023). RecON: Online learning for sensorless freehand 3D ultrasound reconstruction. Med Image Anal., 87:102810.
  11. Song, X., Zhou, F., Frangi, A. F. , Cao, J., Xiao, X., and 3 more authors. (2023). Multi-Center and Multi-Channel Pooling GCN for Early AD Diagnosis Based on Dual-Modality Fused Brain Network. IEEE Trans Med Imaging., 42(2):354–67.
  12. Zakeri, A., Hokmabadi, A., Bi, N., Wijesinghea, I., Nix, M. G., and 3 more authors. (2023). DragNet: learning-based deformable registration for realistic cardiac MR sequence generation from a single frame. Med Image Anal., 83:102678.
  13. Zhang, J., Zhao, Y., SHone, F., Li, Z., Frangi, A. F. , and 2 more authors. (2023). Physics-informed Deep Learning for Musculoskeletal Modelling: Predicting Muscle Forces and Joint Kinematics from Surface EMG. IEEE Trans. Neural Syst. Rehabilitation Eng., 31:484–493.
  14. Zhao, Y., Bao, T., Li, Z., Qian, J., Frangi, A. F. , and 2 more authors. (2023). Boosting Personalised Musculoskeletal Modelling with Physics-informed Knowledge Transfer. IEEE Trans Instrum Meas., 72:1–11.
  15. Frangi, A. F. , Prince, J. L., and Sonka, M. (Eds.). (2023). Medical Image Analysis (Textbook). London, UK: Academic Press.
    ISBN: 978-0-1281-3657-7.
  16. Su, R., Zhang, Y., Liu, H., and Frangi, A. F. . (Eds.). (2023). Medical Imaging and Computer-Aided Diagnosis. Berlin: Springer-Verlag.
    ISBN: 978-9-811-66774-9. Series: Lecture Notes in Electrical Engineering. Volume: 810.
  17. Frangi, A. F. , Denison, T., and Lincoln, J. (2023). The Economic Impact of In-silico Technology on the UK and its Lifesciences Sector. InSilicoUK Innovation Network.
  18. Frangi, A. F. , Denison, T., Brown, P., Turpin, R., Kipping, M., and 20 more authors. (2023). Unlocking the power of computational modelling and simulation across the product lifecycle in life sciences: A UK Landscape Report. Sounder, safer, faster, and more sustainable innovation and regulatory evidence of medicines and healthcare products. InSilicoUK Innovation Network.
  19. Redrup, E., Mitchell, C., Myles, P., Branson, R., and Frangi, A. F. . (2023). Cross-Regulator Workshop: Journeys, experiences and best practices on computer modelled and simulated regulatory evidence— Workshop Report. InSilicoUK Innovation Network.
  20. Whorwood, H., Frangi, A. F. , and Wilkinson, K. (2023). In Silico Medicine: Investment in next-generation life sciences innovations empowered by computational modelling and simulations. Beauhurst.
  21. Xia, Y., Ravikumar, N., and Frangi, A. F. . (2023). Method and apparatus for generating subject-specific magnetic resonance angiography images from other multi-contrast magnetic resonance images. Patent Application No 18/242,982. US Patent and Trademark Office.
  22. Dou, H., Ravikumar, N., and Frangi, A. F. . (2023). Method and Apparatus for Generating Virtual Populations of Anatomy. Patent Application PCT/EP2023/077300. World Intellectual Property Organization.

2022

  1. DiazPinto2021a.jpg
    Diaz-Pinto, A., Attar, R., Ravikumar, N., Suinesiaputra, A., Zhao, Y., and 11 more authors. (2022). Predicting Infarction through your Retinal Scans and Minimal Personal Information. Nat Machine Intel., 4(1):55–61. Available at: https://doi.org/10.1038/s42256-021-00427-7.
  2. Farzi, M., Pozo, J. M., McCoskey, E. V., Eastell, R., Harvey, N. C., and 2 more authors. (2022). Quantitating Age-Related BMD Textural Variation from DXA Region-Free-Analysis: A Study of Hip Fracture Prediction in Three Cohorts. J Bone Miner Res., 37(9):1679–1688.
  3. Frood, R., Clark, M., Burton, C., Tsoumpas, C., Frangi, A. F. , and 3 more authors. (2022). Discovery of Pre-Treatment FDG PET/CT-Derived Radiomics-Based Models for Predicting Outcome in Diffuse Large B-Cell. Cancers, 14(7):1711. DOI: 10.3390/cancers14071711.
  4. Frood, R., Clark, M., Burton, C., Tsoumpas, C., Frangi, A. F. , and 3 more authors. (2022). Utility of pre-treatment FDG PET/CT–derived machine learning models for outcome prediction in classical Hodgkin lymphoma. Eur Radiol, 32(10):7237–7247.
  5. Harkness, R., Hall, G., Frangi, A. F. , Ravikumar, N., and Zucker, K. (2022). The Pitfalls of Using Open Data to Develop Deep Learning Solutions for COVID-19 Detection in Chest X-Rays. Stud Health Technol Inform., 6:679–83.
  6. Huang, Z., Lei, H., Chen, G., Frangi, A. F. , Xu, Y., and 3 more authors. (2022). Parkinson’s Disease Classification and Clinical Score Regression via United Embedding and Sparse Learning from Longitudinal Data. IEEE Trans Neural Netw Learn Syst., 33(8):3357–3371.
  7. Lashgari, M., Ravikumar, N., Teh, I., Li, J.-R., Buckley, D. L., and 2 more authors. (2022). Three-dimensional micro-structurally informed in silico myocardium– towards virtual imaging trials in cardiac diffusion-weighted MRI. Med Image Anal., 82:102592.
  8. Luo, X., Stoyanov, D., Hata, N., Frangi, A. F. , Taylor, R. H., and 1 more author. (2022). Guest Editorial Special Section on Surgical Vision, Navigation, and Robotics. IEEE Trans Med Robot Bionics., 4(1):2–4. DOI: 10.1109/TMRB.2022.3147605.
  9. Xia, Y., Ravikumar, N., and Frangi, A. F. . (2022). Learning to complete incomplete hearts for population analysis of cardiac MR images. Med Image Anal., 77:102354.
  10. Xia, Y., Chen, X., Ravikumar, N., Kelly, C., Attar, R., and 4 more authors. (2022). Automatic 3D+t Four-Chamber CMR Quantification of the UK Biobank: integrating imaging and non-imaging data priors at scale. Med Image Anal., 80:102498.
  11. Zakeri, A., Hokmabadi, A., Ravikumar, N., Frangi, A. F. , and Gooya, A. (2022). A probabilistic deep motion model for unsupervised cardiac shape anomaly assessment. Med Image Anal., 75:e052887.
  12. Kakileti, S. K., Gabrani, M., Manjunath, G., Rosen-Zvi, M., Braman, N., and 5 more authors. (Eds.). (2022). Artificial Intelligence over Infrared Images for Medical Applications and Medical Image Assisted Biomarker Discovery. Berlin: Springer-Verlag.
    ISBN: 978-3-031-19660-7. Series: Lecture Notes in Computer Science. Volume: 13602.
  13. Xia, Y., Ravikumar, N., and Frangi, A. F. . (2022). Image imputation in cardiac MRI and quality assessment. In Burgos, N., and Svoboda, D. (Eds.). Biomedical Image Synthesis and Simulation. (pp. 347–367). Academic Press.
    Series: The MICCAI Society Book Series.
  14. Diaz-Pinto, A., Ravikumar, N., and Frangi, A. F. . (2022). Determination of Cardiac Functional Indexes. Patent Filed No PCT/IB2022/053356. World Intellectual Property Organization.

2021

  1. Attanasio, A., Alberti, C., Scaglioni, B., Marahrens, N., Frangi, A. F. , and 4 more authors. (2021). A Comparative Study of Spatio-Temporal U-Nets for Tissue Segmentation in Surgical Robotics. IEEE Trans Med Robotics Bionics., 3(1):53–63.
  2. Danilov, V. V., Klyshnikov, K. Y., Gerget, O. M., Kutikhin, A. G., Ganyukov, V. I., and 2 more authors. (2021). Real-time coronary artery stenosis detection based on modern neural networks. Sci Rep., 11(1):7582.
  3. Danilov, V. V., Gerget, O. M., Klyshnikov, K. Y., Frangi, A. F. , and Ovcharenko, E. A. (2021). Analysis of Deep Neural Networks for Detection of Coronary Artery Stenosis. Program Comput Softw., 47:153–160.
  4. Dembowska, S., Frangi, A. F. , Houwing-Duistermaat, J., and Liu, H. (2021). Multivariate functional partial least squares for classification using longitudinal data. Theor Biol Forum., 114(1–2):75–88.
  5. Frood, R., Burton, C., Tsoumpas, C., Frangi, A. F. , Gleeson, F., and 2 more authors. (2021). Baseline PET/CT imaging parameters for prediction of treatment outcome in Hodgkin and diffuse large B-cell lymphoma: A Systematic Review. Eur J Nucl Med Mol Imaging., 48(10):3198–3220.
  6. Guo, L., Lei, B., Chen, W., Dua, J., Frangi, A. F. , and 5 more authors. (2021). Dual Attention Enhancement Feature Fusion Network for Segmentation and Quantitative Analysis of Paediatric Echocardiography. Med Image Anal., 71:102042.
  7. Hu, H., Pan, N., Lui, H., Liu, L., Yin, T., and 2 more authors. (2021). Automatic segmentation of left and right ventricles in cardiac MRI using 3D-ASM and deep learning. Signal Process Image Commun., 96:116303.
  8. Lei, B., Cheng, N., Frangi, A. F. , Wei, Y., Yu, B., and 10 more authors. (2021). Auto-weighted Centralised Multi-Task Learning via Integrating Functional and Structural Connectivity for Subjective Cognitive Decline Diagnosis. Med Image Anal., 74:102248.
  9. Mendonca, T. V., Jones, A. A., Pozo, J. M., Baxendale, S., Whilfield, T. T., and 1 more author. (2021). Origami: Single-cell 3D shape dynamics oriented along the apico-basal axis of folding epithelia from fluorescence microscopy. PLoS Comp Biol., 17(11):e1009063.
  10. Nabavi, S., Ejmalian, A., Moghaddam, M. E., Abin, A. A., Frangi, A. F. , and 2 more authors. (2021). Medical imaging and computational image analysis in COVID-19 diagnosis: A review. Comput Biol Med., 135:104605.
  11. Nadarajah, R., Wu, J., Frangi, A. F. , Hogg, D., Cowan, C., and 1 more author. (2021). Predicting patient-level new-onset atrial fibrillation from population-based nationwide electronic health records: protocol of FIND-AF for developing a precision medicine prediction model using artificial intelligence. BMJ Open., 11(11):e052887.
  12. Pal, A., Chaturved, A., Chandra, A., Chatterjee, R., Senapat, S., and 2 more authors. (2021). MICaps: Multi-Instance Capsule Network for Machine Inspection of Munro’s Microabscess. Comp Biol Med., 140:105071. DOI: 10.1016/j.compbiomed.2021.105071.
  13. Sarrami-Foroushani, A., Lassila, T., MacRaild, M., Asquith, J., Roes, K. C. B., and 2 more authors. (2021). In-silico trial of intracranial flow diverters replicates and expands insights from conventional clinical trials: Supplementary material. Nat Comm., 12(1):3861.
  14. Smye, S. W., and Frangi, A. F. . (2021). Interdisciplinary research: shaping the healthcare of the future. Future Healthc J., 8(2):e218–e223.
  15. Song, X., Zhou, F., Frangi, A. F. , Cao, J., Xiao, X., and 3 more authors. (2021). Graph Convolution Network with Similarity Awareness and Adaptive Calibration for Disease-induced Deterioration Prediction. Med Image Anal., 61(101947).
  16. Van den Eynde, J., Manlhiot, C., Van De Bruaene, A., Diller, G.-P., Frangi, A. F. , and 2 more authors. (2021). Medicine-Based Evidence in Congenital Heart Disease: How Artificial Intelligence Can Guide Treatment Decision. Front Cardiovasc Med., 8:798215. DOI: 10.3389/fcvm.2021.798215.
  17. Vukicevic, A. M., Zelic, K., Milasinovic, D., Sarrami‐Foroushani, A., Jovicic, G., and 4 more authors. (2021). OpenMandible: An open-source framework for highly realistic numerical modelling of lower mandible physiology. Dent Mater., 37(4):612–624.
  18. Yang, X., Li, H., Wang, Y., Liang, K., Chen, C., and 6 more authors. (2021). Contrastive Rendering with Semi-supervised Learning for Ovary and Follicle Segmentation from 3D Ultrasound. Med Image Anal., 73:102134.
  19. Frangi, A. F. . (2021). Am (A)I human? Building Safer and More Effective Medical Devices with Virtual Twins. Pint of Science, Leeds. Available at: https://pintofscience.co.uk/event/hear-me-out.
  20. (2021). Clinical Trials for Medicines and New Medical Procedures. BBC World Service Radio. Available at: https://www.bbc.co.uk/programmes/w172xv2pndhs8lj.
  21. (2021). Os ensaios clı́nicos virtuais estão a caminho (e podem ser mais práticos e baratos). aeiou. Available at: https://zap.aeiou.pt/ensaios-clinicos-virtuais-a-caminho-418520.
  22. (2021). Using virtual populations for clinical trials. Science Daily. Available at: https://www.sciencedaily.com/releases/2021/06/210623091139.htm.
  23. (2021). Rewrite the Rules. Lifesciences Integrates. Available at: https://www.lifescienceintegrates.com/medtech-integrates-agenda-2021/#rewrite.
  24. (2021). Replace, Reduce, Refine: In-Silico Trials in The Spotlight. Clinical OMICs. Available at: https://www.clinicalomics.com/topics/patient-care/cardiovascular-disease/replace-reduce-refine-in-silico-trials-in-the-spotlight.
  25. (2021). ’Huge potential’ in virtual clinical trials. University of Leeds. Available at: https://www.leeds.ac.uk/news-health/news/article/4850/huge-potential-in-virtual-clinical-trials.
  26. (2021). Virtual clinical trials are on their way. The Economist. Available at: https://www.economist.com/science-and-technology/2021/07/01/virtual-clinical-trials-are-on-their-way.
  27. (2021). Virtual Patients for Evaluating Medical Devices. BBC Digital Planet. Available at: https://www.bbc.co.uk/programmes/w3ct1lsg.

2020

  1. Abadi, E., Segars, W. P., Tsui, B. M. W., Kinahan, P. E., Bottenus, N., and 4 more authors. (2020). Virtual clinical trials in medical imaging: a review.. J Medical Imaging., 7(4):042805.
  2. Asif, M., Chen, L., Song, H., Yang, J., and Frangi, A. F. . (2020). An Automatic Framework for Endoscopic Image Restoration and Enhancement. Appl Intell., 51(1959-–1971).
  3. Attanasio, A., Scaglione, B., Leonetti, M., Frangi, A. F. , Cross, W., and 2 more authors. (2020). Autonomous Tissue Retraction in Robotic Assisted Minimally Invasive Surgery - A Feasibility Study.. IEEE Robot Autom Lett., 5(4):6528–35.
  4. Fu, T., Yang, J., Li, Q., Ai, D., Song, H., and 3 more authors. (2020). Groupwise Registration with Global-local Graph Shrinkage in Atlas Construction.. Med Image Anal., 64:101711.
  5. Hu, Y., Lei, B., Guo, L., Mao, M., Jin, Z., and 4 more authors. (2020). AIDAN: An Attention-guided Dual-path Network for Pediatric Echocardiography Segmentation. IEEE Access., 8(1):29176–29187.
  6. Lassila, T., Sarrami-Foroushani, A., Hejazi, S. M., and Frangi, A. F. . (2020). Population-specific modelling of between/within-subject flow variability in the carotid arteries of the elderly.. Int J Num Meth Biomed Eng., 36(1):e3271.
  7. Lei, B., Cheng, N., Frangi, A. F. , Tanc, E.-L., Yang, P., and 4 more authors. (2020). Self-calibrated Brain Network Estimation and Joint Non-Convex Multi-Task Learning for Identification of Early Alzheimer’s Disease. Med Image Anal., 61:101652.
  8. Lei, B., Yu, S., Zhao, X., Frangi, A. F. , Tanc, E.-L., and 3 more authors. (2020). Early Alzheimer’s Disease Diagnosis Based on Dynamic High Order Networks. Brain Imaging Behav., 15(1):276–287.
  9. Li, F., Song, D., Chen, H., Xiong, J., Li, X., and 35 more authors. (2020). Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection (iGlaucoma): a multicenter study. Nature Digit Med., 22(3):123.
  10. Littlejohns, T., Holliday, J., Gibson, L., Garratt, S., Oesingmann, N., and 19 more authors. (2020). The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions.. Nat Comm., 11(1):2624–.
  11. Mou, L., Zhao, Y., Fu, H., Liu, Y., Cheng, J., and 7 more authors. (2020). CS2-Net: Deep Learning Segmentation of Curvilinear Structures in Medical Imaging. Med Image Anal., 67:101874.
  12. Vukicevic, A. M., Milic, V., Zabotti, A., Hocevar, A., De Lucia, O., and 5 more authors. (2020). Radiomics-based assessment of Primary Sjögren’s Syndrome from salivary gland ultrasonography images.. IEEE J Biomed Health Inform., 24(3):835–843.
  13. Wang, C., Oda, M., Hayashi, Y., Yoshino, Y., Yamamoto, T., and 2 more authors. (2020). Tensor-cut: A Tensor-based Graph-cut Blood Vessel Segmentation Method and Its Application to Renal Artery Segmentation. Med Image Anal., 60:101623.
  14. Xia, Y., Zhang, L., Ravikumar, N., Attar, R., Piechnik, S., and 3 more authors. (2020). Recovering from Missing Data in Population Imaging - Cardiac MR Image Imputation via Conditional Generative Adversarial Nets. Med Image Anal., 67:101812.

2019

  1. Attar, R., Perenez, M., Gooya, A., Alba, X., Zhang, L., and 11 more authors. (2019). Quantitative CMR Population Imaging on 20,000 Subjects of the UK Biobank Imaging Study: LV/RV Quantification Pipeline and its Evaluation. Med Image Anal., 25(56):26–42.
  2. Coelho, S., Pozo, J. M., Jespersen, S. N., Jones, D. K., and Frangi, A. F. . (2019). Resolving degeneracy in diffusion MRI biophysical model parameter estimation using double diffusion encoding.. Magn Res Med., 82(1):395–41.
  3. Coelho, S., Pozo, J. M., Costantini, M., Highley, J. R., Mozumder, M., and 3 more authors. (2019). Histological data of axons, astrocytes, and myelin in deep subcortical white matter populations.. Data Brief, 6(23):103762.
  4. Diaz-Pinto, A. Y., Colomer, A., Naranjo, V., Morales, S., Xu, Y., and 1 more author. (2019). Retinal Image Synthesis and Semi-supervised Learning for Glaucoma Assessment.. IEEE Trans Med Imaging., 38(9):2211–2218.
  5. Aime, S., Alberich, A., Almen, A., Arthurs, O., Barthel, H., and 40 more authors. (2019). Strategic research agenda for biomedical imaging. Insights into Imaging, 10(7). DOI: 10.1186/s13244-019-0684-z.
  6. Farzi, M., Pozo, J. M., McCloskey, E., Eastell, R., Harvey, N., and 2 more authors. (2019). A Spatio-Temporal Ageing Atlas of the Proximal Femur.. IEEE Trans Med Imaging., 39(5):1359–1368.
  7. Fathi-Kazerooni, A., Pozo, J. M., McCloskey, E. V., Saligheh-Rad, H., and Frangi, A. F. . (2019). Diffusion MRI For Assessment of Bone Quality; A Review of Findings in Healthy Aging and Osteoporosis.. J Magn Res Imaging., 51(4):975–992.
  8. Fehri, H., Gooya, A., Lu, Y., Meijering, E. H. W., Johnston, S., and 1 more author. (2019). Bayesian Polytrees with Learned Deep Features for Multi-Class Cell Segmentation.. IEEE Trans Image Process., 28(7):3246–3260.
  9. Fichtinger, G., Schnabel, J., Davatzikos, C., Frangi, A. F. , and Alberola-López, C. (2019). Editorial IJCARS-MICCAI 2018 Special Issue. Int J Comput Assist Radiol Surg., 14(9):1461.
  10. Mozumder, M., Pozo, J. M., Coelho, S., Costantini, M., Simpson, J., and 3 more authors. (2019). Quantitative histomorphometry of capillary microstructure in deep white matter. NeuroImage: Clin., 25(23):101839.
  11. Mozumder, M., Pozo, J. M., Coelho, S., and Frangi, A. F. . (2019). Population-based Bayesian regularization for microstructural diffusion MRI with NODDIDA. Magn Res Med., 82(4):1553–1565.
  12. Ravikumar, N., Gooya, A., Beltrachini, L., Frangi, A. F. , and Taylor, Z. A. (2019). Generalised coherent point drift for group-wise multi-dimensional analysis of diffusion brain MRI data.. Med Image Anal.(53):47–63.
  13. Sarrami-Foroushani, A., Lassila, T., Hejazi, S. M., Nagaraja, S., Bacon, A., and 1 more author. (2019). A computational model for prediction of clot platelet content in flow-diverted intracranial aneurysms. J Biomech., 25(91):7–13.
  14. Schnabel, J., Davatzikos, C., Fichtinger, G., Frangi, A. F. , and Alberola-López, C. (2019). Editorial: Special Issue on MICCAI 2018.. Med Image Anal., 58:101560.
  15. Shaukat, F., Raja, G., and Frangi, A. F. . (2019). Computer-Aided Detection of Lung Nodules: A Review.. J Med Imaging., 6(2):020901.
  16. Song, S., Du, C., Liu, X., Huang, Y., Song, H., and 5 more authors. (2019). Deep Motion Tracking from Multiview Angiographic Image Sequences for Synchronization of Cardiac Phases.. Phys Med Biol., 64(2):025018.
  17. Song, S., Frangi, A. F. , Yang, J., Ai, D., Du, C., and 5 more authors. (2019). Patch-Based Adaptive Background Subtraction for Vascular Enhancement in X-Ray Cineangiograms.. IEEE J Biomed Health Inform., 23(6):2563–2575.
  18. Vardakis, J. C., Guo, L., Peach, T. W., Lassila, T., Mitolo, M., and 6 more authors. (2019). Fluid-Structure Interaction for Highly Complex, Statistically Defined, Biological Media: Homogenisation and a 3D Multi-Compartmental Poroelastic Model for Brain Biomechanics. J Fluids Struct, 91:102641.
  19. Vardakis, J. C., Bonfanti, M., Franzetti, G., Guo, L., Lassila, T., and 12 more authors. (2019). Highly integrated workflows for exploring cardiovascular conditions: Exemplars of precision medicine in Alzheimer’s disease and aortic dissection.. Morphologie., 103(343):148–160.
  20. Venneri, A., Mitolo, M., Beltrachini, L., Varma, S., Della Pieta, C., and 3 more authors. (2019). Beyond Episodic Memory: Semantic Processing as Independent redictor of Hippocampal/Perirhinal Volume in Aging and Mild Cognitive Impairmenet due to Alzheimer’s Disease. Neuropsychol., 33(4):523–533.
  21. Waller, R., Baxter, L., Fillingham, D. J., Coelho, S., Pozo, J. M., and 5 more authors. (2019). Iba-1-/CD68+ microglia are a prominent feature of age-associated deep subcortical white matter lesions.. PLOS One., 14(1):e0210888.
  22. Yan, Q., Zhao, Y., Zheng, Y., Liu, Y., Zhou, K., and 2 more authors. (2019). Automated retinal lesion detection via image saliency analysis.. Med Phys., 46(10):4531–44.
  23. Zhang, L., Gooya, A., Pereanez, M., Dong, B., Piechnik, S. K., and 3 more authors. (2019). Automatic Assessment of Full Left Ventricular Coverage in Cardiac Cine Magnetic Resonance Imaging with Fisher- Discriminative 3D CNN.. IEEE Trans Biomedical Eng., 60(7):1975–86.

2018

  1. Alba, X., Lekadir, K., Young, A. A., Pereañez, M., Medrano-Gracia, P., and 1 more author. (2018). Automatic Initialization and Quality Control of Large-Scale Cardiac MRI Segmentations.. Med Image Anal.(43):129–145.
  2. Chen, X., Pengfei, J., Yiping, W., Henghui, Z., Liao, W., and 2 more authors. (2018). A Surface-based Approach to Determine Key Spatial Parameters of the Acetabulum in a Standardized Pelvic Coordinate.. Med Eng Phys., 52:22–30.
  3. Coelho, S., Pozo, J. M., Costantini, M., Mozumder, M., Highley, J. R., and 2 more authors. (2018). Local volume fraction distributions of axons, astrocytes, and myelin in deep subcortical white matter.. Neuroimage, 179:275–287.
  4. Fathi-Kazerooni, A., Nabil, M., Zeinali-Zadeh, M., Firouznia, K., Azmoudeh-Ardalan, F., and 3 more authors. (2018). Characterization of Active and Infiltrative Tumorous Subregions from Normal Tissue in Brain Gliomas Using Multi-Parametric MRI.. J Mag Res Imaging., 48(4):938–950.
  5. Frangi, A. F. , Tsaftaris, S. A., and Prince, J. L. (2018). Editorial: Special issue on Simulation and Synthesis in Medical Imaging.. IEEE Trans Med Imaging., 37(3):673–679.
  6. Gooya, A., Lekadir, K., Castro-Mateos, I., Pozo, J. M., and Frangi, A. F. . (2018). Mixture of probabilistic principal component analyzers for shapes from point sets. IEEE Trans Pattern Anal Mach Intell., 40(4):891–904.
  7. Guo, L., Vardakis, J. C., Lassila, T., Mitolo, M., Ravikumar, N., and 9 more authors. (2018). Subject-specific multiporoelastic model for exploring the risk factors associated with the early stages of Alzheimer’s Disease.. Interface Focus., 8(1):e20170019.
  8. Huang, Y., Shao, L., and Frangi, A. F. . (2018). Cross-Modality Image Synthesis via Weakly-Coupled and Geometry Co-Regularized Joint Dictionary Learning.. IEEE Trans Med Imaging., 37(3):815–827.
  9. Lassila, T., Di Marco, L. Y., Mitolo, M., Iaia, V., Levedianos, G., and 2 more authors. (2018). Screening for Cognitive Impairment by Model Assisted Cerebral Blood Flow Estimation.. IEEE Trans Biomedical Eng., 65(7):1654–1661.
  10. Maier-Hein, L., Eisenmann, M., Reinke, A., Onogur, S., Stankovic, M., and 33 more authors. (2018). Why rankings of biomedical image analysis competitions should be interpreted with care?. Nat Commun., 9(1):5217.
  11. Mozumder, M., Beltrachini, L., Collier, Q., Pozo, J. M., and Frangi, A. F. . (2018). Simultaneous magnetic resonance diffusion and pseudo-diffusion tensor imaging. Magn Res Med., 79(4):2367–2378.
  12. Nemat, H., Fehri, H., Ahmadinejad, N., Frangi, A. F. , and Gooya, A. (2018). Classification of Breast Lesions in Ultrasonography Using Sparse Logistic Regression and Morphology-based Texture Features.. Med Phys., 45(9):4112–4124.
  13. Ngoepe, M. N., Frangi, A. F. , Byrne, J. V., and Ventikos, Y. (2018). Thrombosis in cerebral aneurysms and the computational modelling thereof: A review.. Front Physiol- Computational Physiology and Medicine., 9:e00306.
  14. Suinesiaputra, A., Ablin, P., Alba, X., Alessandrini, M., Allen, J., and 31 more authors. (2018). Statistical shape modeling of the left ventricle: myocardial infarct classification challenge.. IEEE J Biomed Health Inform., 22(2):503–515.
  15. Vukicevic, A., Çimen, S., Jagic, N., Jovicic, G., Frangi, A. F. , and 1 more author. (2018). Reconstruction and structured meshing of coronary arteries from X-ray angiography.. Sci Reports., 8(1):1711ff.
  16. Frangi, A. F. , Schnabel, J. A., Davatzikos, C., López-Alberola, C., and Fichtinger, G. (Eds.). (2018). Medical Image Computing and Computer-Assisted Intervention (MICCAI): Proceedings of the International Conference MICCAI 2018 – Part I: Image Quality and Artefacts; Image Reconstruction Methods; Machine Learning in Medical Imaging; Statistical Analysis for Medical Imaging; Image Registration Methods. Berlin: Springer-Verlag.
    ISBN: 978-3-030-00927-4. Series: Lecture Notes in Computer Science. Volume: 11070.
  17. Frangi, A. F. , Schnabel, J. A., Davatzikos, C., López-Alberola, C., and Fichtinger, G. (Eds.). (2018). Medical Image Computing and Computer-Assisted Intervention (MICCAI): Proceedings of the International Conference MICCAI 2018 – Part II: Optical and Histology Applications: Optical Imaging Applications; Histology Applications; Microscopy Applications; Optical Coherence Tomography and Other Optical Imaging Applications. Cardiac, Chest and Abdominal Applications: Cardiac Imaging Applications: Colorectal, Kidney and Liver Imaging Applications; Lung Imaging Applications; Breast Imaging Applications; Other Abdominal Applications. Berlin: Springer-Verlag.
    ISBN: 978-3-030-00933-5. Series: Lecture Notes in Computer Science. Volume: 11071.
  18. Frangi, A. F. , Schnabel, J. A., Davatzikos, C., López-Alberola, C., and Fichtinger, G. (Eds.). (2018). Medical Image Computing and Computer-Assisted Intervention (MICCAI): Proceedings of the International Conference MICCAI 2018 – Part III: Diffusion Tensor Imaging and Functional MRI: Diffusion Tensor Imaging; Diffusion Weighted Imaging; Functional MRI; Human Connectome. Neuroimaging and Brain Segmentation Methods: Neuroimaging; Brain Segmentation Methods.
    Series: Lecture Notes in Computer Science.
  19. Glocker, B., Yao, J., Vrtovec, T., Frangi, A. F. , and Zheng, G. (Eds.). (2018). Computational Methods and Clinical Applications in Musculoskeletal Imaging: Proceedings of the International Workshop and Challenge on Computational Musculoskeletal Imaging (MSKI2017). Berlin: Springer-Verlag.
    ISBN: 978-3-319-74112-3. Series: Lecture Notes in Computer Science. Volume: 10734.

2017

  1. De Marco, M., Beltrachini, L., Biancardi, A., Frangi, A. F. , and Venneri, A. (2017). Machine learning support to individual diagnosis of mild cognitive impairment using multimodal MRI and cognitive assessments. Alzheimer Dis Assoc Disord., 31(4):278–286.
  2. De Marco, M., Vallelunga, A., Meneghello, F., Varma, S., Frangi, A. F. , and 1 more author. (2017). ApoE ε4 Allele Related Alterations in Hippocampal Connectivity in Early Alzheimer’s Disease Support Memory Performance. Curr Alzheimer Res, 14(7):766–77.
  3. Elhami, M., Alemi, N., Frangi, A. F. , and Gooya, A. (2017). Tracking and Diameter Estimation of Retinal Vessels Using Gaussian Process and Radon Transform.. J Med Imaging., 4(3):e034006.
  4. Evju, Ø., Pozo, J. M., Frangi, A. F. , and Mardal, K.-A. (2017). Robustness of common hemodynamic indicators with respect to numerical resolution in 38 middle cerebral artery aneurysms. PLoS One, 12(6):e0177566.
  5. Farzi, M., Morris, R. M., Penny, J., Yang, L., Pozo, J. M., and 3 more authors. (2017). Quantitating the effect of prosthesis design on femoral remodeling using high-resolution region-free densitometric analysis (DXA-RFA). J Orthop Res., 35(10):2203–2210.
  6. Fu, H., Xu, Y., Lin, S., Zhang, X., Wong, D. W. K., and 4 more authors. (2017). Segmentation and Quantification for Angle-Closure Glaucoma Assessment in Anterior Segment OCT. IEEE Trans Med Imaging., 36(9):1930–1938.
  7. Geers, A. J., Morales, H. G., Larrabide, I., Butakoff, C., Bijlenga, P., and 1 more author. (2017). Wall shear stress at the initiation site of cerebral aneurysms. Biomech Model Mechanobiol, 16.
  8. Hoogendoorn, C., Sebastian, R., Rodriguez, J. F., Lekadir, K., and Frangi, A. F. . (2017). An atlas- and data-driven approach to initializing reaction-diffusion systems in computer cardiac electrophysiology.. Int J Numer Method Biomed Eng., 33(8):e2846.
  9. Hua, R., Pozo, J. M., Taylor, Z. A., and Frangi, A. F. . (2017). Multiresolution eXtended Free-Form Deformations (XFFD) for non-rigid registration with discontinuous transforms.. Med Image Anal, 36:113–122.
  10. Kasztelnik, M., Coto, E., Bubak, M., Malawski, M., Nowakowski, P., and 4 more authors. (2017). Support for Taverna workflows in the VPH-Share cloud platform. Comput Methods Programs Biomed, 146:37–46.
  11. Lange, M., Palamara, S., Lassila, T., Vergara, C., Quarteroni, A., and 1 more author. (2017). Improved hybrid/GPU algorithm for solving cardiac electrophysiology problems on Purkinje networks.. Int J Numer Method Biomed Eng., 33(6):e2835.
  12. Manap, R. A., Shao, L., and Frangi, A. F. . (2017). PATCH-IQ: A patch based learning framework for blind image quality assessment.. Inform Sciences., 420:329–344.
  13. McGrath, D. M., Ravikumar, N., Beltrachini, L., Wilkinson, I. D., Frangi, A. F. , and 1 more author. (2017). Evaluation of wave delivery methodology for brain MRE: insights from computational simulations. Magn Reson Med, 78(1):341–356.
  14. Parker, A., Yang, L., Farzi, M., Pozo, J. M., Frangi, A. F. , and 1 more author. (2017). Quantifying Pelvic Periprosthetic Bone Remodeling Using Dual-Energy X-Ray Absorptiometry Region-Free Analysis. J Clin Densitom., 20(4):480–485.
  15. Parto Dezfouli, M. A., Parto Dezfouli, M., Ahmadian, A., Frangi, A. F. , Esmaeili Rad, M., and 1 more author. (2017). Quantification of 1 H-MRS signals based on sparse metabolite profiles in the time-frequency domain.. NMR Biomed., 30(2):e3675. DOI: 10.1002/nbm.3675.
    Keyword: MRS, continuous wavelet transformation (CWT), quantification, sparse representation.
  16. Pozo, J. M., Geers, A. J., Villa-Uriol, M. C., and Frangi, A. F. . (2017). Interlacing Complexity Index for open flow systems based on mutual information. J Fluid Mech., 825:704–742.
  17. Ravikumar, N., Gooya, A., Çimen, S., Frangi, A. F. , and Taylor, Z. A. (2017). Group-wise similarity registration of point sets using Student’s t-mixture model for statistical shape models.. Med Image Anal.(42):156–176.
  18. Sarrami-Foroushani, A., Lassila, T., and Frangi, A. F. . (2017). Virtual endovascular treatment of intracranial aneurysms: models and uncertainty.. Wiley Interdiscip Rev Syst Biol Med, 9(4):e1385.
  19. Shaukat, F., Raja, G., Gooya, A., and Frangi, A. F. . (2017). Fully automatic and accurate detection of lung nodules in CT images using a hybrid feature set.. Med Phys, 44(7):3615–3629.
  20. Liu, C.-L., Lee, S.-W., Yang, J.-Y., Yang, J., Makihara, Y., and 2 more authors. (Eds.). (2017). Proceedings The 4th Asian Conference on Pattern Recognition (ACPR2017). Piscataway, NJ: IEEE Computer Society.
    ISBN: 978-1-5386-3354-0.
  21. Tsaftaris, S. A., Gooya, A., Frangi, A. F. , and Prince, J. L. (Eds.). (2017). Simulation and Synthesis in Medical Imaging: Proceedings of the International Workshop SASHIMI 2017. Berlin: Springer-Verlag.
    ISBN: 978-3-319-68126-9. Series: Lecture Notes in Computer Science. Volume: 10557.
  22. Yao, J., Vrtovec, T., Glocker, B., Zheng, G., Frangi, A. F. , and 1 more author. (Eds.). (2017). Computational Methods and Clinical Applications for Spine Imaging: Proceedings of the International Workshop and Challenge on Computational Spine Imaging (CSI2016). Berlin: Springer-Verlag.
    ISBN: 978-3-319-55049-7. Series: Lecture Notes in Computer Science. Volume: 10182.

2016

  1. Alba, X., Pereanez, M., Hoogendoorn, C., Swift, A. J., Wild, J. M., and 2 more authors. (2016). An algorithm for the segmentation of highly abnormal hearts using a generic statistical shape model. IEEE Trans Med Imaging, 35(3):845–859.
  2. Avegliano, G. P., Costabel, J. P., Asch, F. M., Sciancalepore, A., Kuschnir, P., and 4 more authors. (2016). Utility of real time 3D echocardiography for the assessment of left ventricular mass in patients with hypertrophic cardiomyopathy: comparison with cardiac magnetic resonance. Echocardiogr –J Card, 33(3):431–436.
  3. Butakoff, C., Balocco, S., Sukno, F. M., Hoogendoorn, C., Tobon-Gomez, C., and 2 more authors. (2016). Left-ventricular epi- and endocardium extraction from 3D ultrasound images using an automatically constructed 3D ASM. Comput Methods Biomech Biomed Eng Imaging Vis, 4(5):265–280.
  4. Castro-Mateos, I., Hua, R., Pozo, J. M., Lazary, A., and Frangi, A. F. . (2016). Intervertebral disc classification by its degree of degeneration from T2-weighted magnetic resonance images. Eur Spine J, 25(9):2721–2727.
  5. Çimen, S., Gooya, A., Grass, M., and Frangi, A. F. . (2016). Reconstruction of coronary arteries from x-ray angiography: a review. Med Image Anal, 32:46–68.
  6. Frangi, A. F. , Taylor, Z. A., and Gooya, A. (2016). Precision imaging: more descriptive, predictive and integrative imaging. Med Image Anal, 33:27–32.
  7. Karim, R., Bhagirath, P., Claus, P., James Housden, R., Chen, Z., and 14 more authors. (2016). Evaluation of state-of-the-art segmentation algorithms for left ventricle infarct from late gadolinium enhancement MR images. Med Image Anal, 30:95–107.
  8. Lange, M., Di Marco, L. Y., Lekadir, K., Lassila, T., and Frangi, A. F. . (2016). Protective role of false tendon in subjects with left bundle branch block: a virtual population study. PLoS One, 11(1):e0146477.
  9. Lekadir, K., Noble, C., Hazrati-Marangalou, J., Hoogendoorn, C., Rietbergen, B., and 2 more authors. (2016). Patient-specific biomechanical modeling of bone strength using statistically-derived fabric tensors. Ann Biomed Eng, 44(1):234–246.
  10. Lekadir, K., Lange, M., Zimmer, V. A., Hoogendoorn, C., and Frangi, A. F. . (2016). Statistically-driven 3D fiber reconstruction and denoising from multi-slice cardiac DTI using a Markov random field model. Med Image Anal, 27:105–116.
  11. Lekadir, K., Hoogendoorn, C., Armitage, P., Whitby, E., King, D., and 2 more authors. (2016). Estimation of trabecular bone parameters in children from multisequence MRI using texture-based regression. Med Phys, 43(6):3071.
  12. Manap, R., Shao, L., and Frangi, A. F. . (2016). Non-parametric quality assessment of natural images. IEEE Multimedia, 23(4):22–30.
  13. McGrath, D. M., Ravikumar, N., Wilkinson, I. D., Frangi, A. F. , and Taylor, Z. A. (2016). Magnetic resonance elastography of the brain: an in silico study to determine the influence of cranial anatomy. Magn Reson Med, 76(2):645–62.
  14. Peng, P., Lekadir, K., Gooya, A., Shao, L., Petersen, S. E., and 1 more author. (2016). A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging. MAGMA, 29(2):155–195.
  15. Porras, A. R., Alessandrini, M., Mirea, O., D’hooge, J., Frangi, A. F. , and 1 more author. (2016). Integration of multi-plane tissue Doppler and b-mode echocardiographic images for left ventricular motion estimation. IEEE Trans Med Imaging, 35(1):89–97.
  16. Sarrami-Foroushani, A., Lassila, T., Gooya, A., Geers, A. J., and Frangi, A. F. . (2016). Uncertainty quantification of wall shear stress in intracranial aneurysms using a data-driven statistical model of systemic blood flow variability. J Biomech, 49(16):3815–3823.
  17. Vergara, C., Lange, M., Palamara, S., Lassila, T., Frangi, A. F. , and 1 more author. (2016). A coupled 3D-1D numerical monodomain solver for cardiac electrical activation in the myocardium with detailed Purkinje network. J Comput Phys, 308:218–238.
  18. Yao, J., Burns, J. E., Forsberg, D., Seitel, A., Rasoulian, A., and 11 more authors. (2016). A multi-center milestone study of clinical vertebral CT segmentation. Comput Med Imaging Graph, 49:16–28.
  19. Tsaftaris, S. A., Gooya, A., Frangi, A. F. , and Prince, J. L. (Eds.). (2016). Simulation and Synthesis in Medical Imaging: Proceedings of the International Workshop SASHIMI 2016. Berlin: Springer-Verlag.
    ISBN: 978-3-319-46629-3. Series: Lecture Notes in Computer Science. Volume: 9968.
  20. Vrtovec, T., Yao, J., Glocker, B., Klinder, T., Frangi, A. F. , and 2 more authors. (Eds.). (2016). Computational Methods and Clinical Applications for Spine Imaging: Proceedings of the International Workshop and Challenge Computational Spine Imaging (CSI2015). Berlin: Springer-Verlag.
    ISBN: 978-3-319-41826-1. Series: Lecture Notes in Computer Science. Volume: 9402.
  21. Duchateau, N., Piella, G., Frangi, A. F. , and De Craene, M. (2016). Learning pathological deviations from a normal pattern of myocardial motion: Added value for CRT studies?. In Wu, G., Shen, D., and Sabuncu, M. (Eds.). Machine Learning and Medical Imaging. (pp. 365–82). Elsevier.
  22. Cimen, S., and Frangi, A. F. . (2016). Method and Apparatus for Modelling Non-rigid Networks. Patent Application WO 2016/030692 A1. World Intellectual Property Organization. Available at: https://lens.org/146-615-565-018-562.
  23. Frangi, A. F. . (2016). Café Scientific, Chesterfield. CISTIB. Available at: http://tinyurl.com/zdmrot6.
    Video.

2015

  1. Beltrachini, L., Taylor, Z. A., and Frangi, A. F. . (2015). A parametric finite element solution of the generalised Bloch-Torrey equation for arbitrary domains. J Magn Reson, 259:126–34.
  2. Beltrachini, L., De Marco, M., Taylor, Z. A., Lotjonen, J., Frangi, A. F. , and 1 more author. (2015). Integration of cognitive tests and resting state fMRI for the individual identification of mild cognitive impairment. Curr Alzheimer Res, 12(6):592–603.
  3. Castro-Mateos, I., Pozo, J. M., Pereanez, M., Lekadir, K., Lazary, A., and 1 more author. (2015). Statistical interspace models (SIMs): application to robust 3D spine segmentation. IEEE Trans Med Imaging, 34(8):1663–75.
  4. Cito, S., Geers, A. J., Arroyo, M. P., Palero, V. R., Pallares, J., and 11 more authors. (2015). Accuracy and Reproducibility of Patient-Specific Hemodynamic Models of Stented Intracranial Aneurysms: Report on the Virtual Intracranial Stenting Challenge 2011. Ann Biomed Eng, 43(1):154–167.
  5. Di Marco, L. Y., Venneri, A., Farkas, E., Evans, P. C., Marzo, A., and 1 more author. (2015). Vascular dysfunction in the pathogenesis of Alzheimer’s disease–a review of endothelium-mediated mechanisms and ensuing vicious circles. Neurobiol Dis, 82:593–606.
  6. Di Marco, L. Y., Farkas, E., Martin, C., Venneri, A., and Frangi, A. F. . (2015). Is vasomotion in cerebral arteries impaired in Alzheimer’s disease?. J Alzheimers Dis, 46(1):35–53.
  7. Dimitri, P., Jacques, R. M., Paggiosi, M., King, D., Walsh, J., and 4 more authors. (2015). Leptin may play a role in bone microstructural alterations in obese children. J Clin Endocrinol Metab, 100(2):594–602.
  8. Gooya, A., Davatzikos, C., and Frangi, A. F. . (2015). A Bayesian approach to sparse model selection in statistical shape models. SIAM J Imag Sci, 8(2):858–887.
  9. Lekadir, K., Hoogendoorn, C., Hazrati-Marangalou, J., Taylor, Z. A., Noble, C., and 2 more authors. (2015). A predictive model of vertebral trabecular anisotropy from ex vivo micro-CT. IEEE Trans Med Imaging, 34(8):1747–59.
  10. Lekadir, K., Hazrati-Marangalou, J., Hoogendoorn, C., Taylor, Z. A., van Rietbergen, B., and 1 more author. (2015). Statistical estimation of femur micro-architecture using optimal shape and density predictors. J Biomech, 48(4):598–603.
  11. Malandrino, A., Pozo, J. M., Castro-Mateos, I., Frangi, A. F. , Rijsbergen, M. M., and 5 more authors. (2015). On the relative relevance of subject-specific geometries and degeneration-specific mechanical properties for the study of cell death in human intervertebral disk models. Front Bioeng Biotechnol, 3:5.
  12. Morris, R. M., Yang, L., Martı́n-Fernández, M. A., Pozo, J. M., Frangi, A. F. , and 1 more author. (2015). High-spatial-resolution bone densitometry with dual-energy x-ray absorptiometric region-free analysis. Radiology, 274(2):532–9.
  13. Pereañez, M., Lekadir, K., Castro-Mateos, I., Pozo, J. M., Lazáry, \., and 1 more author. (2015). Accurate segmentation of vertebral bodies and processes using statistical shape decomposition and conditional models. IEEE Trans Med Imaging, 34(8):1627–39.
  14. Sarrami-Foroushani, A., Villa-Uriol, M. C., Nasr Esfahany, M., Coley, S. C., Di Marco, L. Y., and 2 more authors. (2015). Modeling of the acute effects of primary hypertension and hypotension on the hemodynamics of intracranial aneurysms. Ann Biomed Eng, 43(1):207–21.
  15. Sarrami-Foroushani, A., Nasr Esfahany, M., Nasiraei Moghaddam, A., Saligheh Rad, H., Firouznia, K., and 4 more authors. (2015). Velocity measurement in carotid artery: quantitative comparison of time-resolved 3D phase-contrast MRI and image-based computational fluid dynamics. Iran J Radiol, 12(4).
  16. Wilkinson, J. M., Morris, R. M., Martin-Fernandez, M. A., Pozo, J. M., Frangi, A. F. , and 2 more authors. (2015). Use of high resolution dual-energy x-ray absorptiometry-region free analysis (DXA-RFA) to detect local periprosthetic bone remodeling events. J Orthop Res, 33(5):712–6. DOI: 10.1002/jor.22823. Available at: http://www.ncbi.nlm.nih.gov/pubmed/25640686.
  17. Zimmer, V. A., Lekadir, K., Hoogendoorn, C., Frangi, A. F. , and Piella, G. (2015). A framework for optimal kernel-based manifold embedding of medical image data. Comput Med Imaging Graph, 41:93–107.
  18. Navab, N., Hornegger, J., Wells, W. M., and Frangi, A. F. . (Eds.). (2015). Medical Image Computing and Computer-Assisted Intervention (MICCAI): Proceedings of the International Conference MICCAI 2015 – Part I: Advanced MRI: Diffusion, fMRI, DCE; Computer Assisted and Image-guided Interventions; Computer Aided Diagnosis: Machine Learning. Berlin: Springer-Verlag.
    ISBN: 978-3-319-24552-2. Series: Lecture Notes in Computer Science. Volume: 9349.
  19. Navab, N., Hornegger, J., Wells, W. M., and Frangi, A. F. . (Eds.). (2015). Medical Image Computing and Computer-Assisted Intervention (MICCAI): Proceedings of the International Conference MICCAI 2015 – Part II: Quantitative Image Analysis II: Classification, Detection, Features, and Morphology, Advanced MRI: Diffusion, fMRI, DCE; Quantitative Image Analysis III: Motion, Deformation, Development and Degeneration; Quantitative Image Analysis IV: Microscopy, Fluorescence and Histological Imagery. Berlin: Springer-Verlag.
    ISBN: 978-3-319-24570-6. Series: Lecture Notes in Computer Science. Volume: 9350.
  20. Navab, N., Hornegger, J., Wells, W. M., and Frangi, A. F. . (Eds.). (2015). Medical Image Computing and Computer-Assisted Intervention (MICCAI): Proceedings of the International Conference MICCAI 2015 – Part III: Quantitative Image Analysis I: Segmentation and Measurement; Quantitative Image Analysis IV: Microscopy, Fluorescence and Histological Imagery; Quantitative Image Analysis III: Motion, Deformation, Development and Degeneration; Quantitative Image Analysis II: Classification, Detection, Features, and Morphology. Berlin: Springer-Verlag.
    ISBN: 978-3-319-24573-7. Series: Lecture Notes in Computer Science. Volume: 9351.
  21. Radaelli, A. G., Bogunović, H., Villa-Uriol, M. C., Cebral, J. R., and Frangi, A. F. . (2015). Image-based haemodynamics simulation in intracranial aneurysms. In Paragios, N., Duncan, J., and Ayache, N. (Eds.). Handbook of Biomedical Imaging: Methodologies and Clinical Research. (pp. 199–217). Boston, MA: Springer US.
  22. Barbarito, V., Carotenuto, L., Serra Del Molino, L., Frangi, A. F. , Brugada, J., and 1 more author. (2015). Computer Implemented Methods for Identifying Channels in a 3D Volume and Computer Program Product Implementing the Methods. Patent Application US 2015/0356742 A1. United States. Available at: https://lens.org/086-877-529-831-869.
  23. Frangi, A. F. . (2015). MySpine Project Overview. EuroNews. Available at: http://tinyurl.com/jd8e8nm.
    Video.
  24. Frangi, A. F. . (2015). Can Computer Heal Spines? Do you know?. EuroNews. Available at: http://tinyurl.com/jjozs4r.
    Video.
  25. Frangi, A. F. . (2015). Sheffield Festival of Science & Engineering, Interview. CISTIB. Available at: http://tinyurl.com/he3w45x.
    Video.
  26. Frangi, A. F. . (2015). VPH-DARE-IT Platforms Overview. CISTIB. ICT2015 Conference. Available at: http://tinyurl.com/jcxhphh.
    Video.
  27. Frangi, A. F. . (2015). VPH-DARE@IT Clinical Research Platforms Overview. CISTIB. ICT2015 Conference. Available at: http://tinyurl.com/jd2s2hq.
    Video.

2014

  1. Alba, X., Figueras I Ventura, R. M., Lekadir, K., Tobon-Gomez, C., Hoogendoorn, C., and 1 more author. (2014). Automatic cardiac lv segmentation in MRI using modified graph cuts with smoothness and interslice constraints. Magn Reson Med, 72(6):1775–1784.
  2. Avegliano, G., Costabel, J. P., Huguet, M., Thierer, J., Trivi, M., and 5 more authors. (2014). Influence of dynamic obstruction and hypertrophy location on diastolic function in hypertrophic cardiomyopathy. J Cardiovasc Med, 15(3):207–213.
  3. Castro-Mateos, I., Pozo, J. M., Cootes, T. F., Wilkinson, J. M., Eastell, R., and 1 more author. (2014). Statistical shape and appearance models in osteoporosis. Curr Osteoporos Rep, 12(2):163–173.
  4. Di Marco, L. Y., Marzo, A., Munoz-Ruiz, M., Ikram, M. A., Kivipelto, M., and 6 more authors. (2014). Modifiable lifestyle factors in dementia: a systematic review of longitudinal observational cohort studies. J Alzheimers Dis, 42(1):119–135.
  5. Geers, A. J., Larrabide, I., Morales, H. G., and Frangi, A. F. . (2014). Approximating hemodynamics of cerebral aneurysms with steady flow simulations. J Biomech, 47(1):178–185.
  6. Lekadir, K., Pashaei, A., Hoogendoorn, C., Pereanez, M., Alba, X., and 1 more author. (2014). Effect of statistically derived fiber models on the estimation of cardiac electrical activation. IEEE Trans Biomed Eng, 61(11):2740–2748.
  7. Lekadir, K., Hoogendoorn, C., Pereanez, M., Alba, X., Pashaei, A., and 1 more author. (2014). Statistical personalization of ventricular fiber orientation using shape predictors. IEEE Trans Med Imaging, 33(4):882–890.
  8. Moosavi, M.-H., Fatouraee, N., Katoozian, H., Pashaei, A., Camara, O., and 1 more author. (2014). Numerical simulation of blood flow in the left ventricle and aortic sinus using magnetic resonance imaging and computational fluid dynamics. Comput Methods Biomech Biomed Engin, 17(7):740–749.
  9. Pavani, S.-K., Delgado-Gomez, D., and Frangi, A. F. . (2014). Gaussian weak classifiers based on co-occurring Haar-like features for face detection. Pattern Anal Appl, 17(2):431–439.
  10. Pavani, S.-K., Delgado-Gomez, D., and Frangi, A. F. . (2014). Fast training procedure for Viola-Jones type object detectors using Laplacian clutter models. Pattern Anal Appl, 17(2):441–449.
  11. Pereanez, M., Lekadir, K., Butakoff, C., Hoogendoorn, C., and Frangi, A. F. . (2014). A framework for the merging of pre-existing and correspondenceless 3D statistical shape models. Med Image Anal, 18(7):1044–1058.
  12. Porras, A. R., Alessandrini, M., De Craene, M., Duchateau, N., Sitges, M., and 6 more authors. (2014). Improved myocardial motion estimation combining tissue Doppler and b-mode echocardiographic images. IEEE Trans Med Imaging, 33(11):2098–2106.
  13. Porras, A. R., Piella, G., Berruezo, A., Fernández-Armenta, J., and Frangi, A. F. . (2014). Pre to Intraoperative Data Fusion Framework for Multimodal Characterization of Myocardial Scar Tissue.. IEEE J Transl Eng Health Med., 4(2):1900211.
  14. Barbarito, V., Carotenuto, L., Serra Del Molino, L., Frangi, A. F. , Brugada, J., and 1 more author. (2014). Computer Implemented Methods for Identifying Channels in a 3D Volume and Computer Program Product Implementing the Methods. Patent Application WO 2014/111787 A1. World Intellectual Property Organization. Available at: https://lens.org/077-893-349-918-223.

2013

  1. Bijlenga, P., Ebeling, C., Jaegersberg, M., Summers, P., Rogers, A., and 13 more authors. (2013). Risk of rupture of small anterior communicating artery aneurysms is similar to posterior circulation aneurysms. Stroke, 44(11):3018–3026.
  2. Bogunovic, H., Pozo, J. M., Cardenes, R., San Roman, L., and Frangi, A. F. . (2013). Anatomical labeling of the Circle of Willis using maximum a posteriori probability estimation. IEEE Trans Med Imaging, 32(9):1587–1599.
  3. Cardenes, R., Diez, J. L., Duchateau, N., Pashaei, A., and Frangi, A. F. . (2013). Model generation of coronary artery bifurcations from CTA and single plane angiography. Med Phys, 40(1):e013701.
  4. Cardenes, R., Larrabide, I., San Roman, L., and Frangi, A. F. . (2013). Performance assessment of isolation methods for geometrical cerebral aneurysm analysis. Med Biol Eng Comput, 51(3):343–352.
  5. Fernandez-Armenta, J., Berruezo, A., Andreu, D., Camara, O., Silva, E., and 11 more authors. (2013). Three-dimensional architecture of scar and conducting channels based on high resolution ce-cmr: insights for ventricular tachycardia ablation. Circ Arrhythm Electrophysiol, 6(3):528–537.
  6. Frangi, A. F. , Hose, D. R., Hunter, P. J., Ayache, N., and Brooks, D. (2013). Special issue on medical imaging and image computing in computational physiology. IEEE Trans Med Imaging, 32(1):1–7.
  7. Hoogendoorn, C., Duchateau, N., Sanchez-Quintana, D., Whitmarsh, T., Sukno, F. M., and 3 more authors. (2013). A high-resolution atlas and statistical model of the human heart from multislice ct. IEEE Trans Med Imaging, 32(1):28–44.
  8. Hunter, P., Chapman, T., Coveney, P. V., Bono, B., Diaz, V., and 19 more authors. (2013). A vision and strategy for the virtual physiological human: 2012 update. Interface Focus, 3(2).
  9. Larrabide, I., Aguilar, M. L., Morales, H. G., Geers, A. J., Kulcsar, Z., and 2 more authors. (2013). Intra-aneurysmal pressure and flow changes induced by flow diverters: relation to aneurysm size and shape. Am J Neuroradiol, 34(4):816–822.
  10. Marchesseau, S., Delingette, H., Sermesant, M., Cabrera-Lozoya, R., Tobon-Gomez, C., and 14 more authors. (2013). Personalization of a cardiac electromechanical model using reduced order unscented Kalman filtering from regional volumes. Med Image Anal, 17(7):816–829.
  11. Marti Fuster, B., Esteban, O., Planes, X., Aguiar, P., Crespo, C., and 9 more authors. (2013). FocusDet, a new toolbox for SISCOM analysis. evaluation of the registration accuracy using Monte Carlo simulation. Neuroinformatics, 11(1):77–89.
  12. Morales, H. G., Larrabide, I., Geers, A. J., San Roman, L., Blasco, J., and 2 more authors. (2013). A virtual coiling technique for image-based aneurysm models by dynamic path planning. IEEE Trans Med Imaging, 32(1):119–129.
  13. Morales, H. G., Larrabide, I., Geers, A. J., Aguilar, M. L., and Frangi, A. F. . (2013). Newtonian and non-newtonian blood flow in coiled cerebral aneurysms. J Biomech, 46(13):2158–2164.
  14. Morales, H. G., Larrabide, I., Geers, A. J., Dai, D., Kallmes, D. F., and 1 more author. (2013). Analysis and quantification of endovascular coil distribution inside saccular aneurysms using histological images. J Neurointerv Surg, 5:III33–III37.
  15. Perez, F., Huguet, J., Aguilar, R., Lara, L., Larrabide, I., and 12 more authors. (2013). RadStation3G: a platform for cardiovascular image analysis integrating pacs, 3D+t visualization and grid computing. Comput Methods Programs Biomed, 110(3):399–410.
  16. Piella, G., De Craene, M., Butakoff, C., Grau, V., Yao, C., and 3 more authors. (2013). Multiview diffeomorphic registration: application to motion and strain estimation from 3D echocardiography. Med Image Anal, 17(3):348–364.
  17. Porras, A. R., Piella, G., Berruezo, A., Hoogendoorn, C., Andreu, D., and 3 more authors. (2013). Interventional endocardial motion estimation from electroanatomical mapping data: application to scar characterization. IEEE Trans Biomed Eng, 60(5):1217–1224.
  18. Sebastian, R., Zimmerman, V., Romero, D., Sanchez-Quintana, D., and Frangi, A. F. . (2013). Characterization and modeling of the peripheral cardiac conduction system. IEEE Trans Med Imaging, 32(1):45–55.
  19. Tobon-Gomez, C., De Craene, M., McLeod, K., Tautz, L., Shi, W., and 20 more authors. (2013). Benchmarking framework for myocardial tracking and deformation algorithms: an open access database. Med Image Anal, 17(6):632–648.
  20. Tobon-Gomez, C., Duchateau, N., Sebastian, R., Marchesseau, S., Camara, O., and 17 more authors. (2013). Understanding the mechanisms amenable to CRT response: from pre-operative multimodal image data to patient-specific computational models. Med Biol Eng Comput, 51(11):1235–1250.
  21. Weese, J., Groth, A., Nickisch, H., Barschdorf, H., Weber, F. M., and 15 more authors. (2013). Generating anatomical models of the heart and the aorta from medical images for personalized physiological simulations. Med Biol Eng Comput, 51(11):1209–1219.
  22. Whitmarsh, T., Humbert, L., Del Rio Barquero, L. M., Di Gregorio, S., and Frangi, A. F. . (2013). 3D reconstruction of the lumbar vertebrae from anteroposterior and lateral dual-energy x-ray absorptiometry. Med Image Anal, 17(4):475–487.
  23. Humbert, L., Whitmarsh, T., Del Rio Barquero, L., De Craene, M., and Frangi, A. F. . (2013). Metodo para Obtener una Reconstruccion Tridimensional a Partir de una o mas Vistas Proyectivas, y uso de la misma. Granted Patent ES 2382774 B1. Spain. Available at: https://lens.org/088-548-851-354-429.

2012

  1. Bernardini, A., Larrabide, I., Petrini, L., Pennati, G., Fiore, E., and 2 more authors. (2012). Deployment of self-expandable stents in aneurysmatic cerebral vessels: comparison of different computational approaches for interventional planning. Comput Methods Biomech Biomed Engin, 15(3):303–311.
  2. Bogunovic, H., Pozo, J. M., Cardenes, R., Cruz Villa-Uriol, M., Blanc, R., and 2 more authors. (2012). Automated landmarking and geometric characterization of the carotid siphon. Med Image Anal, 16(4):889–903.
  3. Cerrolaza, J. J., Villanueva, A., Sukno, F. M., Butakoff, C., Frangi, A. F. , and 1 more author. (2012). Full multiresolution active shape models. J Math Imaging Vision, 44(3):463–479.
  4. De Craene, M., Piella, G., Camara, O., Duchateau, N., Silva, E., and 5 more authors. (2012). Temporal diffeomorphic free-form deformation: application to motion and strain estimation from 3D echocardiography. Med Image Anal, 16(2):427–450.
  5. Duchateau, N., Doltra, A., Silva, E., De Craene, Y., Piella, G., and 5 more authors. (2012). Atlas-based quantification of myocardial motion abnormalities: added-value for understanding the effect of cardiac resynchronization therapy. Ultrasound Med Biol, 38(12):2186–2197.
  6. Duchateau, N., De Craene, M., Piella, G., and Frangi, A. F. . (2012). Constrained manifold learning for the characterization of pathological deviations from normality. Med Image Anal, 16(8):1532–1549.
  7. Duckett, S. G., Camara, O., Ginks, M. R., Bostock, J., Chinchapatnam, P., and 9 more authors. (2012). Relationship between endocardial activation sequences defined by high-density mapping to early septal contraction (septal flash) in patients with left bundle branch block undergoing cardiac resynchronization therapy. Europace, 14(1):99–106.
  8. Humbert, L., Whitmarsh, T., del Rio Barquero, L. M., and Frangi, A. F. . (2012). Computing structural parameters from dual-energy x-ray absorptiometry using a 3D reconstruction method. Osteoporos Int, 23:S349–S350.
  9. Humbert, L., Whitmarsh, T., De Craene, M., del Rio Barquero, L. M., and Frangi, A. F. . (2012). Technical note: comparison between single and multiview simulated DXA configurations for reconstructing the 3D shape and bone mineral density distribution of the proximal femur. Med Phys, 39(8):5272–5276.
  10. Larrabide, I., Kim, M., Augsburger, L., Cruz Villa-Uriol, M., Ruefenacht, D., and 1 more author. (2012). Fast virtual deployment of self-expandable stents: method and in vitro evaluation for intracranial aneurysmal stenting. Med Image Anal, 16(3):721–730.
  11. Larrabide, I., Villa-Uriol, M.-C., Cardenes, R., Barbarito, V., Carotenuto, L., and 9 more authors. (2012). Angiolab-a software tool for morphological analysis and endovascular treatment planning of intracranial aneurysms. Comput Methods Programs Biomed, 108(2):806–819.
  12. Oubel, E., De Craene, M., Hero, A. O., Pourmorteza, A., Huguet, M., and 3 more authors. (2012). Cardiac motion estimation by joint alignment of tagged MRI sequences. Med Image Anal, 16(1):339–350.
  13. Pavani, S.-K., Sukno, F. M., Delgado-Gomez, D., Butakoff, C., Planes, X., and 1 more author. (2012). An experimental evaluation of three classifiers for use in self-updating face recognition systems. IEEE Trans Inf Forensics Security, 7(3):932–943.
  14. Tobon-Gomez, C., Sukno, F. M., Butakoff, C., Huguet, M., and Frangi, A. F. . (2012). Automatic training and reliability estimation for 3D ASM applied to cardiac MRI segmentation. Phys Med Biol, 57(13):4155–4174.
  15. Whitmarsh, T., Fritscher, K. D., Humbert, L., del Rio Barquero, L. M., Roth, T., and 4 more authors. (2012). Hip fracture discrimination from dual-energy x-ray absorptiometry by statistical model registration. Bone, 51(5):896–901.
  16. Hunter, P. J., Bradley, C., Britten, R., Brooks, D., Carotenuto, L., and 12 more authors. (2012). The VPH-Physiome Project: standards, tools and databases for multi-scale physiological modelling. In Ambrosi, D., Quarteroni, A., and Rozza, G. (Eds.). Modelling Physiological Flows. (pp. 205–250). Springer.
  17. Humbert, L., Whitmarsh, T., Del Rio Barquero, L., De Craene, M., and Frangi, A. F. . (2012). Method for Obtaining a Three-dimensional Reconstruction from One or More Projective Views and Use Thereof. Patent Application WO 2011/098895 A8. World Intellectual Property Organization. Available at: https://lens.org/093-001-489-209-561.
  18. Humbert, L., Whitmarsh, T., Del Rio Barquero, L., De Craene, M., and Frangi, A. F. . (2012). Method for Obtaining a Three-dimensional Reconstruction from One or More Projective Views and Use Thereof. Patent Application EP 2534641 A2. European Patent Office. Available at: https://lens.org/031-317-640-459-582.
  19. Humbert, L., Whitmarsh, T., Del Rio Barquero, L., De Craene, M., and Frangi, A. F. . (2012). Metodo para Obtener una Reconstruccion Tridimensional a Partir de una o mas Vistas Proyectivas, y uso de la misma. Patent Application ES 2382774 A1. Spain. Available at: https://lens.org/108-942-953-738-684.

2011

  1. Avegliano, G., Huguet, M., Costabel, J. P., Ronderos, R., Bijnens, B., and 5 more authors. (2011). Morphologic pattern of late gadolinium enhancement in takotsubo cardiomyopathy detected by early cardiovascular magnetic resonance. Clin Cardiol, 34(3):178–182.
  2. Avegliano, G. P., Huguet, M., Costabel, J. P., Kuschnir, P., Thierer, J., and 5 more authors. (2011). Utilidad de la resonancia magnética cardı́aca en la valoración de los pacientes con dolor torácico, troponinas elevadas y ausencia de obstrucción arterial coronaria. Rev Argent Cardiol, 79(3):226–230.
  3. Bernardini, A., Larrabide, I., Morales, H. G., Pennati, G., Petrini, L., and 2 more authors. (2011). Influence of different computational approaches for stent deployment on cerebral aneurysm haemodynamics. Interface Focus, 1(3):338–348.
  4. Bogunovic, H., Pozo, J. M., Villa-Uriol, M. C., Majoie, C. B. L. M., van den Berg, R., and 5 more authors. (2011). Automated segmentation of cerebral vasculature with aneurysms in 3DRA and tof-mra using geodesic active regions: an evaluation study. Med Phys, 38(1):210–222.
  5. Bradley, C., Bowery, A., Britten, R., Budelmann, V., Camara, O., and 26 more authors. (2011). OpenCMISS: a multi-physics & multi-scale computational infrastructure for the vph/physiome project. Prog Biophys Mol Biol, 107(1):32–47.
  6. Camara, O., Sermesant, M., Lamata, P., Wang, L., Pop, M., and 16 more authors. (2011). Inter-model consistency and complementarity: learning from ex-vivo imaging and electrophysiological data towards an integrated understanding of cardiac physiology. Prog Biophys Mol Biol, 107(1):122–133.
  7. Cardenes, R., Pozo, J. M., Bogunovic, H., Larrabide, I., and Frangi, A. F. . (2011). Automatic aneurysm neck detection using surface Voronoi diagrams. IEEE Trans Med Imaging, 30(10):1863–76.
  8. Coatrieux, J.-L., Frangi, A. F. , Peng, G. C. Y., D’Argenio, D. Z., Marmarelis, V. Z., and 1 more author. (2011). Special issue on Multiscale modeling and analysis in computational biology and medicine - Part 2. IEEE Trans Biomed Eng, 58(12):3434–3439.
  9. Costalat, V., Sanchez, M., Ambard, D., Thines, L., Lonjon, N., and 17 more authors. (2011). Biomechanical wall properties of human intracranial aneurysms resected following surgical clipping (irras project). J Biomech, 44(15):2685–2691.
  10. Duchateau, N., De Craene, M., Piella, G., Silva, E., Doltra, A., and 3 more authors. (2011). A spatiotemporal statistical atlas of motion for the quantification of abnormal myocardial tissue velocities. Med Image Anal, 15(3):316–328.
  11. Frangi, A. F. , Coatrieux, J.-L., Peng, G. C. Y., D’Argenio, D. Z., Marmarelis, V. Z., and 1 more author. (2011). Special issue on Multiscale modeling and analysis in computational biology and medicine - Part 1. IEEE Trans Biomed Eng, 58(10):2936–2942.
  12. Geers, A. J., Larrabide, I., Radaelli, A. G., Bogunovic, H., Kim, M., and 4 more authors. (2011). Patient-specific computational hemodynamics of intracranial aneurysms from 3D rotational angiography and CT angiography: an in vivo reproducibility study. Am J Neuroradiol, 32(3):581–586.
  13. Larrabide, I., Cruz Villa-Uriol, M., Cardenes, R., Pozo, J. M., Macho, J., and 6 more authors. (2011). Three-dimensional morphological analysis of intracranial aneurysms: a fully automated method for aneurysm sac isolation and quantification. Med Phys, 38(5):2439–2449.
  14. Marzo, A., Singh, P., Larrabide, I., Radaelli, A., Coley, S., and 7 more authors. (2011). Computational hemodynamics in cerebral aneurysms: the effects of modeled versus measured boundary conditions. Ann Biomed Eng, 39(2):884–896.
  15. Morales, H. G., Kim, M., Vivas, E. E., Villa-Uriol, M. C., Larrabide, I., and 3 more authors. (2011). How do coil configuration and packing density influence intra-aneurysmal hemodynamics?. Am J Neuroradiol, 32(10):1935–1941.
  16. Pashaei, A., Romero, D., Sebastian, R., Camara, O., and Frangi, A. F. . (2011). Fast multiscale modeling of cardiac electrophysiology including Purkinje system. IEEE Trans Biomed Eng, 58(10):2956–2960.
  17. Pozo, J. M., Villa-Uriol, M.-C., and Frangi, A. F. . (2011). Efficient 3D geometric and Zernike moments computation from unstructured surface meshes. IEEE Trans Pattern Anal Mach Intell, 33(3):471–484.
  18. Sebastian, R., Zimmerman, V., Romero, D., and Frangi, A. F. . (2011). Construction of a computational anatomical model of the peripheral cardiac conduction system. IEEE Trans Biomed Eng, 58(12):3479–3482.
  19. Smith, N., Vecchi, A., McCormick, M., Nordsletten, D., Camara, O., and 18 more authors. (2011). euHeart: personalized and integrated cardiac care using patient-specific cardiovascular modelling. Interface Focus, 1(3):349–364.
  20. Suinesiaputra, A., Frangi, A. F. , Kaandorp, T. A. M., Lamb, H. J., Bax, J. J., and 2 more authors. (2011). Automated regional wall motion abnormality detection by combining rest and stress cardiac MRI: correlation with contrast-enhanced MRI. J Magn Reson Imaging, 34(2):270–278.
  21. Tobon-Gomez, C., Sukno, F. M., Bijnens, B. H., Huguet, M., and Frangi, A. F. . (2011). Realistic simulation of cardiac magnetic resonance studies modeling anatomical variability, trabeculae, and papillary muscles. Magn Reson Med, 65(1):280–288.
  22. Villa-Uriol, M. C., Berti, G., Hose, D. R., Marzo, A., Chiarini, A., and 7 more authors. (2011). @neurIST complex information processing toolchain for the integrated management of cerebral aneurysms. Interface Focus, 1(3):308–319.
  23. Whitmarsh, T., Humbert, L., De Craene, M., del Rio Barquero, L. M., and Frangi, A. F. . (2011). Reconstructing the 3D shape and bone mineral density distribution of the proximal femur from dual-energy x-ray absorptiometry. IEEE Trans Med Imaging, 30(12):2101–2114.
  24. Zhang, C., Villa-Uriol, M.-C., De Craene, M., Pozo, J. M., Macho, J. M., and 1 more author. (2011). Dynamic estimation of three-dimensional cerebrovascular deformation from rotational angiography. Med Phys, 38(3):1294–1306.
  25. Humbert, L., Whitmarsh, T., Del Rio Barquero, L., De Craene, M., and Frangi, A. F. . (2011). Method for Obtaining a Three-dimensional Reconstruction from One or More Projective Views and Use Thereof. Patent Application WO 2011/098895 A2. World Intellectual Property Organization. Available at: https://lens.org/118-958-078-427-819.
  26. Humbert, L., Whitmarsh, T., Del Rio Barquero, L., De Craene, M., and Frangi, A. F. . (2011). Method for Obtaining a Three-dimensional Reconstruction from One or More Projective Views and Use Thereof. Search report WO 2011/098895 A3. World Intellectual Property Organization. Available at: https://lens.org/101-601-983-252-554.
  27. Frangi, A. F. . (2011). DISCIPULUS Project – Digital Patient: Interview. University of Sheffield. Available at: http://tinyurl.com/jrnx9tx.
    Video.
  28. Frangi, A. F. . (2011). CISTIB YouTube Channel. CISTIB. Available at: https://www.youtube.com/user/CISTIB.
    Video.

2010

  1. Balocco, S., Camara, O., Vivas, E., Sola, T., Guimaraens, L., and 5 more authors. (2010). Feasibility of estimating regional mechanical properties of cerebral aneurysms in vivo. Med Phys, 37(4):1689–1706.
  2. Balocco, S., Basset, O., Courbebaisse, G., Boni, E., Frangi, A. F. , and 2 more authors. (2010). Estimation of the viscoelastic properties of vessel walls using a computational model and Doppler ultrasound. Phys Med Biol, 55(12):3557–3575.
  3. Benkner, S., Arbona, A., Berti, G., Chiarini, A., Dunlop, R., and 16 more authors. (2010). @neurIST: infrastructure for advanced disease management through integration of heterogeneous data, computing, and complex processing services. IEEE Trans Inf Technol Biomed, 14(6):1365–1377.
  4. Butakoff, C., and Frangi, A. F. . (2010). Multi-view face segmentation using fusion of statistical shape and appearance models. Comput Vis Image Underst, 114(3):311–321.
  5. Duchateau, N., De Craene, M., Piella, G., Silva, E., Doltra, A., and 3 more authors. (2010). Quantification of septal motion abnormalities in CRT candidates using a statistical atlas based-approach. Eur Heart J, 31:875–876.
  6. Gianni, D., McKeever, S., Yu, T., Britten, R., Delingette, H., and 3 more authors. (2010). Sharing and reusing cardiovascular anatomical models over the web: a step towards the implementation of the virtual physiological human project. Philos Trans A Math Phys Eng Sci, 368(1921):3039–3056.
  7. Hunter, P., Coveney, P. V., Bono, B., Diaz, V., Fenner, J., and 16 more authors. (2010). A vision and strategy for the virtual physiological human in 2010 and beyond. Philos Trans A Math Phys Eng Sci, 368(1920):2595–2614.
  8. Ortega-Garcia, J., Fierrez, J., Alonso-Fernandez, F., Galbally, J., Freire, M. R., and 27 more authors. (2010). The multiscenario multienvironment biosecure multimodal database (BMDB). IEEE Trans Pattern Anal Mach Intell, 32(6):1097–1111.
  9. Oubel, E., Cebral, J. R., De Craene, M., Blanc, R., Blasco, J., and 3 more authors. (2010). Wall motion estimation in intracranial aneurysms. Physiol Meas, 31(9):1119–1135.
  10. Pavani, S.-K., Delgado, D., and Frangi, A. F. . (2010). Haar-like features with optimally weighted rectangles for rapid object detection. Pattern Recognit, 43(1):160–172.
  11. Piella, G., De Craene, M., Bijnens, B. H., Tobon-Gomez, C., Huguet, M., and 2 more authors. (2010). Characterizing myocardial deformation in patients with left ventricular hypertrophy of different etiologies using the strain distribution obtained by magnetic resonance imaging. Rev Esp Cardiol, 63(11):1281–1291.
  12. Romero, D., Sebastian, R., Bijnens, B. H., Zimmerman, V., Boyle, P. M., and 2 more authors. (2010). Effects of the Purkinje system and cardiac geometry on biventricular pacing: a model study. Ann Biomed Eng, 38(4):1388–1398.
  13. Singh, P. K., Marzo, A., Staicu, C., William, M. G., Wilkinson, I., and 7 more authors. (2010). The effects of aortic coarctation on cerebral hemodynamics and its importance in the etiopathogenesis of intracranial aneurysms. J Vasc Interv Neurol, 3(1):17–30.
  14. Singh, P. K., Marzo, A., Howard, B., Rufenacht, D. A., Bijlenga, P., and 5 more authors. (2010). Effects of smoking and hypertension on wall shear stress and oscillatory shear index at the site of intracranial aneurysm formation. Clin Neurol Neurosurg, 112(4):306–313.
  15. Sukno, F. M., Guerrero, J. J., and Frangi, A. F. . (2010). Projective active shape models for pose-variant image analysis of quasi-planar objects: application to facial analysis. Pattern Recognit, 43(3):835–849.
  16. Villa-Uriol, M. C., Larrabide, I., Pozo, J. M., Kim, M., Camara, O., and 7 more authors. (2010). Toward integrated management of cerebral aneurysms. Philos Trans A Math Phys Eng Sci, 368(1921):2961–2982.
  17. Yasuno, K., Bilguvar, K., Bijlenga, P., Low, S.-K., Krischek, B., and 64 more authors. (2010). Genome-wide association study of intracranial aneurysm identifies three new risk loci. Nat Genet, 42(5):420–U69.
  18. Yilmaz, S., Bijlenga, P., Rashid, M., Collot-Teixeira, S., Brocheton, J., and 17 more authors. (2010). Gene expression signature in peripheral blood cells detects intracranial aneurysm. Neurosurgery, 67(2):540.
  19. Frangi, A. F. . (2010). The @neurIST Project: Outreach Video. CISTIB. Available at: http://tinyurl.com/hl5vzvg.
    Video.

2009

  1. Camara, O., Oeltze, S., De Craene, M., Sebastian, R., Silva, E., and 5 more authors. (2009). Detecting abnormal septal motion by combining spatial and electrical information from endocardial mapping data in CRT candidates. Eur Heart J, 30:1015.
  2. Castro, M., Putman, C., Radaelli, A., Frangi, A. F. , and Cebral, J. R. (2009). Hemodynamics and rupture of terminal cerebral aneurysms. Acad Radiol, 16(10):1201–1207.
  3. Delgado-Gomez, D., Fagertun, J., Ersboll, B., Sukno, F. M., and Frangi, A. F. . (2009). Similarity-based fisherfaces. Pattern Recognit Lett, 30(12):1110–1116.
  4. Hoogendoorn, C., Sukno, F. M., Ordas, S., and Frangi, A. F. . (2009). Bilinear models for spatio-temporal point distribution analysis. Int J Comput Vision, 85(3):237–252.
  5. Huguet, M., Tobon-Gomez, C., Bijnens, B. H., Frangi, A. F. , and Petit, M. (2009). Cardiac injuries in blunt chest trauma. J Cardiovasc Magn Reson, 11(35). DOI: doi.org/10.1186/1532-429X-11-35.
  6. Meinhardt, E., Zacur, E., Frangi, A. F. , and Caselles, V. (2009). 3D edge detection by selection of level surface patches. J Math Imaging Vision, 34(1):1–16.
  7. Sebastian, R., Bijnens, B. H., and Frangi, A. F. . (2009). The role of the myocardial fiber orientation in homogenizing transmural electrical activation. Eur Heart J, 30:72.
  8. Singh, P. K., Marzo, A., Coley, S. C., Berti, G., Bijlenga, P., and 7 more authors. (2009). The role of computational fluid dynamics in the management of unruptured intracranial aneurysms: a clinicians’ view. Comput Intell Neurosci:760364.
  9. Suinesiaputra, A., Frangi, A. F. , Kaandorp, T. A. M., Lamb, H. J., Bax, J. J., and 2 more authors. (2009). Automated detection of regional wall motion abnormalities based on a statistical model applied to multislice short-axis cardiac MR images. IEEE Trans Med Imaging, 28(4):595–607. DOI: 10.1109/tmi.2008.2008966.
  10. Young, A. A., and Frangi, A. F. . (2009). Computational cardiac atlases: from patient to population and back. Exp Physiol, 94(5):578–596.
  11. Zhang, C., Villa-Uriol, M.-C., De Craene, M., Pozo, J. M., and Frangi, A. F. . (2009). Morphodynamic analysis of cerebral aneurysm pulsation from time-resolved rotational angiography. IEEE Trans Med Imaging, 28(7):1105–1116.

2008

  1. Laclaustra, M., Frangi, A. F. , Frangi, A. G., Casasnovas, J. A., and Cia, P. (2008). Association of endothelial function and vascular data with ldl-c and HDL-c in a homogeneous population of middle-aged, healthy military men: evidence for a critical role of optimal lipid levels. Int J Cardiol, 125(3):376–382.
  2. Radaelli, A. G., Augsburger, L., Cebral, J. R., Ohta, M., Ruefenacht, D. A., and 11 more authors. (2008). Reproducibility of haemodynamical simulations in a subject-specific stented aneurysm model - a report on the virtual intracranial stenting challenge 2007. J Biomech, 41(10):2069–2081.
  3. Sukno, F. M., and Frangi, A. F. . (2008). Reliability estimation for statistical shape models. IEEE Trans Image Process, 17(12):2442–2455.
  4. Tobon-Gomez, C., Butakoff, C., Aguade, S., Sukno, F., Moragas, G., and 1 more author. (2008). Automatic construction of 3D-ASM intensity models by simulating image acquisition: application to myocardial gated SPECT studies. IEEE Trans Med Imaging, 27(11):1655–1667.
  5. Dunlop, R., Arbona, A., Rajasekaran, H., Lo Iacono, L., Fingberg, J., and 11 more authors. (2008). @neurIST - chronic disease management through integration of heterogeneous data and computer-interpretable guideline services. In Solomonides, T., Silverstein, J. C., Saltz, J., Legre, Y., Kratz, M., and 3 more authors. (Eds.). Global Healthgrid: E-science Meets Biomedical Informatics. (pp. 173–177).
    Series: Studies in Health Technology and Informatics. Volume: 138.
  6. Iavindrasana, J., Lo Iacono, L., Mueller, H., Periz, I., Summers, P., and 16 more authors. (2008). The @neurIST project. In Solomonides, T., Silverstein, J. C., Saltz, J., Legre, Y., Kratz, M., and 3 more authors. (Eds.). Global Healthgrid: E-science Meets Biomedical Informatics. (pp. 161–164).
    Series: Studies in Health Technology and Informatics. Volume: 138.
  7. Frangi, A. F. . (2008). The @neurIST Project: Biomedical data integration supporting in silico understanding of cerebral aneurysms and individualized disease management and interventional planning. The Parliament Magazines Research Review, 7:52–53.

2007

  1. Barber, D. C., Oubel, E., Frangi, A. F. , and Hose, D. R. (2007). Efficient computational fluid dynamics mesh generation by image registration. Med Image Anal, 11(6):648–662.
  2. Hernandez, M., and Frangi, A. F. . (2007). Non-parametric geodesic active regions: method and evaluation for cerebral aneurysms segmentation in 3DRA and CTA. Med Image Anal, 11(3):224–241.
  3. Laclaustra, M., Frangi, A. F. , Garcia, D., Boisrobert, L., Frangi, A. G., and 1 more author. (2007). Detailed exploration of the endothelium: parameterization of flow-mediated dilation through principal component analysis. Physiol Meas, 28(3):301–320.
  4. Millan, R. D., Dempere-Marco, L., Pozo, J. M., Cebral, J. R., and Frangi, A. F. . (2007). Morphological characterization of intracranial aneurysms using 3D moment invariants. IEEE Trans Med Imaging, 26(9):1270–1282.
  5. Olafsdottir, H., Darvann, T. A., Hermann, N. V., Oubel, E., Ersboll, B. K., and 5 more authors. (2007). Computational mouse atlases and their application to automatic assessment of craniofacial dysmorphology caused by the Crouzon mutation fgfr2(c342y). J Anat, 211(1):37–52.
  6. Sukno, F. M., Ordas, S., Butakoff, C., Cruz, S., and Frangi, A. F. . (2007). Active shape models with invariant optimal features: application to facial analysis. IEEE Trans Pattern Anal Mach Intell, 29(7):1105–1117.
  7. Tobon-Gomez, C., Ordas, S., Frangi, A. F. , Aguade, S., and Castell, J. (2007). Statistical deformable models for cardiac segmentation and functional analysis in gated-SPECT studies. In Suri, J., and Farag, A. (Eds.). Deformable Models: Biomedical and Clinical Applications. (pp. 163–193). Springer.
  8. Frangi, A. F. , Ruiz, A., and Hofmann, M. (2007). Understanding cerebral aneurysms: The @neurIST project. ERCIM News. Available at: http://tinyurl.com/y7sfqyoq.
    Special Theme on the Digital Patient.
  9. Frangi, A. F. , Hose, R. D., and Ruefenacht, D. A. (2007). The @neurIST Project: Towards understanding of cerebral aneurysms. SPIE Newsroom. Available at: http://tinyurl.com/ycexcalq.

2006

  1. Assen, H. C., Danilouchkine, M. G., Frangi, A. F. , Ordas, S., Westenberg, J. J. M., and 2 more authors. (2006). SPASM: a 3D-ASM for segmentation of sparse and arbitrarily oriented cardiac MRI data. Med Image Anal, 10(2):286–303.
  2. Butakoff, C., and Frangi, A. F. . (2006). A framework for weighted fusion of multiple statistical models of shape and appearance. IEEE Trans Pattern Anal Mach Intell, 28(11):1847–1857.
  3. Frangi, A. F. , Radeva, P. I., and Santos, A. (2006). Special Issue on International Conference on Functional Imaging and Modelling of the Heart (FIMH). Med Image Anal, 10(4):612–614.
  4. Frangi, A. F. , and Delingette, H. (Eds.). (2006). From Statistical Atlases to Personalized Models: Understanding Complex Diseases in Populations and Individuals: Proceedings of the International Workshop. Copenhagen: IT University of Copenhagen.
    Series: Technical Report.
  5. Arbona, A., Benkner, S., Fingberg, J., Frangi, A. F. , Hofmann, M., and 4 more authors. (2006). Outlook for grid service technologies within the @neurIST eHealth environment. In Hernandez, V., and Blanquer, I. (Eds.). Challenges and Opportunities of Healthgrids. (pp. 401–404).
    Series: Studies in Health Technology and Informatics. Volume: 120.

2005

  1. Cebral, J. R., Castro, M. A., Appanaboyina, S., Putman, C. M., Millan, D., and 1 more author. (2005). Efficient pipeline for image-based patient-specific analysis of cerebral aneurysm hemodynamics: technique and sensitivity. IEEE Trans Med Imaging, 24(4):457–467.
  2. Frangi, A. F. , Amini, A. A., and Bullitt, E. (2005). Vascular imaging. IEEE Trans Med Imaging, 24(4):433–435.
  3. Yang, J., Frangi, A. F. , Yang, J. Y., Zhang, D., and Jin, Z. (2005). Kpca plus LDA: a complete kernel fisher discriminant framework for feature extraction and recognition. IEEE Trans Pattern Anal Mach Intell, 27(2):230–244.
  4. Frangi, A. F. , Radeva, P., Santos, A., and Hernandez, M. (Eds.). (2005). Functional Imaging and Modelling of the Heart (FIMH): Proceedings of the International Conference. Berlin: Springer-Verlag.
    ISBN: 978-35-4026-161-2. Series: Lecture Notes in Computer Science. Volume: 3504.
  5. Frangi, A. F. , Laclaustra, M., and Yang, J. (2005). Computerized analysis and vasodilation parameterization in flow-mediated dilation tests from ultrasonic image sequences. In Suri, J. S., Wilson, D. L., and Laxminarayan, S. (Eds.). Handbook of Biomedical Image Analysis, Vol II: Segmentation Models Pt B. (pp. 229–266). Kluwer Academic Publisher.
    Volume: 2.
  6. Frangi, A. F. , Niessen, W. J., Viergever, M. A., and Lelieveldt, B. P. F. (2005). A survey of three-dimensional modelling techniques for quantitative functional analysis of cardiac images. In Landini, L., and Santarelli, M. F. (Eds.). Advanced Image Processing in Magnetic Resonance Imaging. (pp. 267–341). CRC Pres.
    Volume: 26.
  7. Hernandez, M., Frangi, A. F. , and Sapiro, G. (2005). Quantification of brain aneurysm dimensions from CTA for surgical planning of coiling interventions. In Suri, J. A., Wilson, D. L., and Laxminarayan, S. (Eds.). Handbook of Biomedical Image Analysis, Vol III: Registration Models. (pp. 185–217). Kluwer Academic Publisher.
  8. Ordas, S., van Assen, H. C., Puente, J., Lelieveldt, B. P. F., and Frangi, A. F. . (2005). Parametric optimization of a model-based segmentation algorithm for cardiac MR image analysis: a grid-computing approach. In Solomonides, T., and McClatchey, R. (Eds.). From Grid to Healthgrid. (pp. 146–156).
    Series: Studies in Health Technology and Informatics. Volume: 112.

2004

  1. Yang, J., Frangi, A. F. , and Yang, J. Y. (2004). A new kernel Fisher discriminant algorithm with application to face recognition. Neurocomputing, 56:415–421. DOI: 10.1016/s0925-2312(03)00444-2.
  2. Yang, J., Zhang, D., Frangi, A. F. , and Yang, J. Y. (2004). Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans Pattern Anal Mach Intell, 26(1):131–137.
  3. Yang, J., Jin, Z., Yang, J. Y., Zhang, D., and Frangi, A. F. . (2004). Essence of kernel fisher discriminant: KPCA plus LDA. Pattern Recognit, 37(10):2097–2100.

2003

  1. Frangi, A. F. , Laclaustra, M., and Lamata, P. (2003). A registration-based approach to quantify flow-mediated dilation (FMD) of the brachial artery in ultrasound image sequences. IEEE Trans Med Imaging, 22(11):1458–1469.
  2. Rueckert, D., Frangi, A. F. , and Schnabel, J. A. (2003). Automatic construction of 3-D statistical deformation models of the brain using nonrigid registration. IEEE Trans Med Imaging, 22(8):1014–1025.
  3. Yang, H., Yang, J. Y., and Frangi, A. F. . (2003). Combined fisherfaces framework. Image Vision Comput, 21(12):1037–1044. DOI: 10.1016/j.imavis.2003.07.005.
  4. Yang, R., Yang, J. Y., Frangi, A. F. , and Zhang, D. (2003). Uncorrelated projection discriminant analysis and its application to face image feature extraction. Int J Pattern Recognit Artif Intell, 17(8):1325–1347.

2002

  1. Frangi, A. F. , Riu, P. J., Rosell, J., and Viergever, M. A. (2002). Propagation of measurement noise through backprojection reconstruction in electrical impedance tomography. IEEE Trans Med Imaging, 21(6):566–578.
  2. Frangi, A. F. , Rueckert, D., Schnabel, J. A., and Niessen, W. J. (2002). Automatic construction of multiple-object three-dimensional statistical shape models: application to cardiac modeling. IEEE Trans Med Imaging, 21(9):1151–1166.
  3. Frangi, A. F. , Rueckert, D., and Duncan, J. S. (2002). Three-dimensional cardiovascular image analysis. IEEE Trans Med Imaging, 21(9):1005–1010.
  4. Ginneken, B., Frangi, A. F. , Staal, J. J., Romeny, B. M. T., and Viergever, M. A. (2002). Active shape model segmentation with optimal features. IEEE Trans Med Imaging, 21(8):924–933.
  5. Wink, O., Frangi, A. F. , Verdonck, B., Viergever, M. A., and Niessen, W. J. (2002). 3D MRA coronary axis determination using a minimum cost path approach. Magn Reson Med, 47(6):1169–1175. DOI: 10.1002/mrm.10164.

2001

  1. Frangi, A. F. , Niessen, W. J., and Viergever, M. A. (2001). Three-dimensional modeling for functional analysis of cardiac images: a review. IEEE Trans Med Imaging, 20(1):2–25.
  2. Frangi, A. F. , Niessen, W. J., Nederkoorn, P. J., Bakker, J., Mali, W., and 1 more author. (2001). Quantitative analysis of vascular morphology from 3D MR angiograms: in vitro and in vivo results. Magn Reson Med, 45(2):311–322.
  3. Frangi, A. F. , Egmont-Petersen, M., Niessen, W. J., Reiber, J. H. C., and Viergever, M. A. (2001). Bone tumor segmentation from MR perfusion images with neural networks using multi-scale pharmacokinetic features. Image Vision Comput, 19(9-10):679–690.
  4. Frangi, A. F. . (2001). Three-dimensional Model-based Analysis of Vascular and Cardiac Images. Wageningen, The Netherlands: Ponsen & Looijen.
    ISBN: 90-393-2647-9.

1999

  1. Frangi, A. F. , Niessen, W. J., Hoogeveen, R. M., Walsum, T., and Viergever, M. A. (1999). Model-based quantitation of 3-D magnetic resonance angiographic images. IEEE Trans Med Imaging, 18(10):946–956.