M
Mohsen Ghafoorian
Researcher at Radboud University Nijmegen
Publications - 42
Citations - 11069
Mohsen Ghafoorian is an academic researcher from Radboud University Nijmegen. The author has contributed to research in topics: Convolutional neural network & Segmentation. The author has an hindex of 21, co-authored 42 publications receiving 7575 citations. Previous affiliations of Mohsen Ghafoorian include Brigham and Women's Hospital & Sharif University of Technology.
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Journal ArticleDOI
A survey on deep learning in medical image analysis
Geert Litjens,Thijs Kooi,Babak Ehteshami Bejnordi,Arnaud Arindra Adiyoso Setio,Francesco Ciompi,Mohsen Ghafoorian,Jeroen van der Laak,Bram van Ginneken,Clara I. Sánchez +8 more
TL;DR: This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year, to survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks.
Journal ArticleDOI
Longitudinal multiple sclerosis lesion segmentation: Resource and challenge.
Aaron Carass,Snehashis Roy,Amod Jog,Jennifer L. Cuzzocreo,Elizabeth Magrath,Adrian Gherman,Julia Button,James Nguyen,Ferran Prados,Carole H. Sudre,Manuel Jorge Cardoso,Niamh Cawley,Olga Ciccarelli,Claudia A. M. Wheeler-Kingshott,Sebastien Ourselin,Laurence Catanese,Hrishikesh Deshpande,Pierre Maurel,Olivier Commowick,Christian Barillot,Xavier Tomas-Fernandez,Xavier Tomas-Fernandez,Simon K. Warfield,Simon K. Warfield,Suthirth Vaidya,Abhijith Chunduru,Ramanathan Muthuganapathy,Ganapathy Krishnamurthi,Andrew Jesson,Tal Arbel,Oskar Maier,Heinz Handels,Leonardo O. Iheme,Devrim Unay,Saurabh Jain,Diana M. Sima,Dirk Smeets,Mohsen Ghafoorian,Bram Platel,Ariel Birenbaum,Hayit Greenspan,Pierre-Louis Bazin,Peter A. Calabresi,Ciprian M. Crainiceanu,Lotta Maria Ellingsen,Lotta Maria Ellingsen,Daniel S. Reich,Jerry L. Prince,Dzung L. Pham +48 more
TL;DR: A quantitative evaluation comparing the consistency of the two raters as well as exploring the performance of the eleven submitted results in addition to three other lesion segmentation algorithms are presented.
Book ChapterDOI
Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation
Mohsen Ghafoorian,Mohsen Ghafoorian,Alireza Mehrtash,Alireza Mehrtash,Tina Kapur,Nico Karssemeijer,Elena Marchiori,Mehran Pesteie,Charles R.G. Guttmann,Frank-Erik de Leeuw,Clare M. Tempany,Bram van Ginneken,Andriy Fedorov,Purang Abolmaesumi,Bram Platel,William M. Wells +15 more
TL;DR: In this paper, a CNN was trained on legacy MR images of brain and evaluated the performance of the domain-adapted network on the same task with images from a different domain, and compared the model to the surrogate scenarios where either the same trained network is used or a new network is trained from scratch on the new dataset.
Journal ArticleDOI
Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities.
Mohsen Ghafoorian,Nico Karssemeijer,Tom Heskes,Inge W.M. van Uden,Clara I. Sánchez,Geert Litjens,Frank-Erik de Leeuw,Bram van Ginneken,Elena Marchiori,Bram Platel +9 more
TL;DR: This paper applies and compares the proposed architectures for segmentation of white matter hyperintensities in brain MR images on a large dataset and observes that the CNNs that incorporate location information substantially outperform a conventional segmentation method with handcrafted features as well asCNNs that do not integrate location information.
Journal ArticleDOI
Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge
Hugo J. Kuijf,Adrià Casamitjana,D. Louis Collins,Mahsa Dadar,Achilleas Georgiou,Mohsen Ghafoorian,Dakai Jin,April Khademi,Jesse Knight,Hongwei Li,Xavier Lladó,J. Matthijs Biesbroek,Miguel Luna,Qaiser Mahmood,Richard McKinley,Alireza Mehrtash,Sebastien Ourselin,Bo-yong Park,Hyunjin Park,Sang-Hyun Park,Simon Pezold,Elodie Puybareau,Jeroen de Bresser,Leticia Rittner,Carole H. Sudre,Sergi Valverde,Verónica Vilaplana,Roland Wiest,Yongchao Xu,Ziyue Xu,Guodong Zeng,Jianguo Zhang,Guoyan Zheng,Rutger Heinen,Christopher Chen,Wiesje M. van der Flier,Frederik Barkhof,Max A. Viergever,Geert Jan Biessels,Simon Andermatt,Mariana P. Bento,Matt Berseth,Mikhail Belyaev,M. Jorge Cardoso +43 more
TL;DR: There is a cluster of four methods that rank significantly better than the other methods, with one clear winner, and the inter-scanner robustness ranking shows that not all the methods generalize to unseen scanners.