World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
72
Citations
33425
World Ranking
1639
National Ranking
89

Research.com Recognitions

  • 2021 - IEEE EMBS Technical Achievement Award For pioneering contributions in model-based image computing and image-based computational modelling in medicine with clinical and innovation impact
  • 2021 - MICCAI Fellow For outstanding contributions to computational medical imaging
  • 2020 - SPIE Fellow
  • 2014 - IEEE Fellow For contributions to medical image analysis and image-based computational physiology
  • 2013 - EAMBES Fellow
  • 2006 - IEEE EMBS Early Career Award for outstanding contributions to medical image computing, especially cardiovascular and cerebrovascular image analysis using model- and registration-based methods
  • 2004 - Ramón y Cajal Research Fellowship

Overview

Alejandro F. Frangi is affiliated with the University of Manchester in the United Kingdom. Their research output spans multiple publications and contributions in the fields of Medicine and Computer Science, with significant focus on subfields including Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Artificial Intelligence, Biomedical Engineering, and Cardiology and Cardiovascular Medicine.

The main topics explored in their work include Radiomics and Machine Learning in Medical Imaging, Medical Image Segmentation Techniques, AI in cancer detection, Advanced MRI Techniques and Applications, Cardiac Imaging and Diagnostics, Advanced Neural Network Applications, and Retinal Imaging and Analysis.

Frequent coauthors of Alejandro F. Frangi include Nishant Ravikumar, Haoran Dou, Yan Xia, Dong Ni, and Baiying Lei.

Publications appear primarily in notable venues such as arXiv (Cornell University), Medical Image Analysis, IEEE Transactions on Medical Imaging, npj Digital Medicine, and Computers in Biology and Medicine.

Recent papers feature the following:

  • The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions (2020, Nature Communications)
  • CS 2-Net: Deep learning segmentation of curvilinear structures in medical imaging (2020, Medical Image Analysis)
  • Virtual clinical trials in medical imaging: a review (2020, Journal of Medical Imaging)
  • Physics-Informed Deep Learning for Musculoskeletal Modeling: Predicting Muscle Forces and Joint Kinematics From Surface EMG (2022, IEEE Transactions on Neural Systems and Rehabilitation Engineering)
  • FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare (2025, BMJ)

Alejandro F. Frangi has contributed to several books published by Springer Science+Business Media and the European Organization for Nuclear Research. Titles include Information Processing in Medical Imaging (2023), Medical Imaging and Computer-Aided Diagnosis (2023), Proceedings of 2023 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2023) (2024), and Unlocking the power of computational modelling and simulation across the product lifecycle in life sciences: A UK Landscape Report (2023).

The scientist has been recognized with the SPIE Fellow award in 2020 and the IEEE Fellow award in 2014, the latter awarded for contributions to medical image analysis and image-based computational physiology.

Best Publications

  • Muliscale Vessel Enhancement Filtering

    Alejandro F. Frangi;Wiro J. Niessen;Koen L. Vincken;Max A. Viergever

  • Two-dimensional PCA: a new approach to appearance-based face representation and recognition

    Jian Yang;D. Zhang;A.F. Frangi;Jing-yu Yang

  • Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015

    Nassir Navab;Joachim Hornegger;William M. Wells;Alejandro F. Frangi

  • KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition

    Jian Yang;A.F. Frangi;Jing-Yu Yang;David Zhang

  • Three-dimensional modeling for functional analysis of cardiac images, a review

    A.F. Frangi;W.J. Niessen;M.A. Viergever

  • Active shape model segmentation with optimal features

    B. van Ginneken;A.F. Frangi;J.J. Staal;B.M. ter Haar Romeny

  • Efficient pipeline for image-based patient-specific analysis of cerebral aneurysm hemodynamics: technique and sensitivity

    J.R. Cebral;M.A. Castro;S. Appanaboyina;C.M. Putman

  • Automatic construction of 3-D statistical deformation models of the brain using nonrigid registration

    D. Rueckert;A.F. Frangi;J.A. Schnabel

  • Automatic construction of multiple-object three-dimensional statistical shape models: application to cardiac modeling

    A.F. Frangi;D. Rueckert;J.A. Schnabel;W.J. Niessen

  • Model-based quantitation of 3-D magnetic resonance angiographic images

    A.F. Frangi;W.J. Niessen;R.M. Hoogeveen;T. van Walsum

  • CS2-Net: Deep learning segmentation of curvilinear structures in medical imaging.

    Lei Mou;Yitian Zhao;Huazhu Fu;Yonghuai Liu

  • Why rankings of biomedical image analysis competitions should be interpreted with care

    Lena Maier-Hein;Matthias Eisenmann;Annika Reinke;Sinan Onogur

  • A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging

    Peng Peng;Karim Lekadir;Ali Gooya;Ling Shao

  • The Multiscenario Multienvironment BioSecure Multimodal Database (BMDB)

    Javier Ortega-Garcia;Julian Fierrez;Fernando Alonso-Fernandez;Javier Galbally

  • SPASM: A 3D-ASM for segmentation of sparse and arbitrarily oriented cardiac MRI data

    Hans C. van Assen;Mikhail G. Danilouchkine;Alejandro F. Frangi;Sebastián Ordás

  • CS-Net: Channel and Spatial Attention Network for Curvilinear Structure Segmentation

    Lei Mou;Yitian Zhao;Li Chen;Jun Cheng

  • Essence of kernel Fisher discriminant

    Jian Yang;Zhong Jin;Jing-yu Yang;David Zhang

  • Automatic Construction of 3D Statistical Deformation Models Using Non-rigid Registration

    Daniel Rueckert;Alejandro F. Frangi;Alejandro F. Frangi;Julia A. Schnabel

  • Retinal Image Synthesis and Semi-Supervised Learning for Glaucoma Assessment

    Andres Diaz-Pinto;Adrian Colomer;Valery Naranjo;Sandra Morales

  • Benchmarking framework for myocardial tracking and deformation algorithms: an open access database.

    C. Tobon-Gomez;M. De Craene;M. De Craene;K. McLeod;L. Tautz

  • Virtual clinical trials in medical imaging: a review

    Ehsan Abadi;William P. Segars;Benjamin M. W. Tsui;Paul E. Kinahan

  • Medical image analysis

    Baba C. Vemuri;James S. Duncan

Frequent Co-Authors

Wiro J. Niessen
Wiro J. Niessen Erasmus University Rotterdam
Peter Hunter
Peter Hunter University of Auckland
Boudewijn P. F. Lelieveldt
Boudewijn P. F. Lelieveldt Leiden University Medical Center
Max A. Viergever
Max A. Viergever Utrecht University
Daniel Rueckert
Daniel Rueckert Technical University of Munich
Maxime Sermesant
Maxime Sermesant Université Côte d'Azur
Julia A. Schnabel
Julia A. Schnabel King's College London
Ling Shao
Ling Shao Terminus International
Annalena Venneri
Annalena Venneri Brunel University London
Alfio Quarteroni
Alfio Quarteroni Polytechnic University of Milan

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