G
Gerard Sanroma
Researcher at Pompeu Fabra University
Publications - 48
Citations - 1848
Gerard Sanroma is an academic researcher from Pompeu Fabra University. The author has contributed to research in topics: Segmentation & Image segmentation. The author has an hindex of 13, co-authored 47 publications receiving 1107 citations. Previous affiliations of Gerard Sanroma include Rovira i Virgili University & University of North Carolina at Chapel Hill.
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Journal ArticleDOI
Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?
Olivier Bernard,Alain Lalande,Clement Zotti,Frederick Cervenansky,Xin Yang,Pheng-Ann Heng,Irem Cetin,Karim Lekadir,Oscar Camara,Miguel Ángel González Ballester,Gerard Sanroma,Sandy Napel,Steffen E. Petersen,Georgios Tziritas,Elias Grinias,Mahendra Khened,Varghese Alex Kollerathu,Ganapathy Krishnamurthi,Marc-Michel Rohé,Xavier Pennec,Maxime Sermesant,Fabian Isensee,Paul F. Jäger,Klaus H. Maier-Hein,Peter M. Full,Ivo Wolf,Sandy Engelhardt,Christian F. Baumgartner,Lisa M. Koch,Jelmer M. Wolterink,Ivana Išgum,Yeonggul Jang,Yoonmi Hong,Jay Patravali,Shubham Jain,Olivier Humbert,Pierre-Marc Jodoin +36 more
TL;DR: How far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies is measured, to open the door to highly accurate and fully automatic analysis of cardiac CMRI.
Journal ArticleDOI
Benchmark on Automatic Six-Month-Old Infant Brain Segmentation Algorithms: The iSeg-2017 Challenge
Li Wang,Dong Nie,Guannan Li,Elodie Puybareau,Jose Dolz,Qian Zhang,Fan Wang,Jing Xia,Zhengwang Wu,Jia-Wei Chen,Kim-Han Thung,Toan Duc Bui,Jitae Shin,Guodong Zeng,Guoyan Zheng,Vladimir S. Fonov,Andrew Doyle,Yongchao Xu,Pim Moeskops,Josien P. W. Pluim,Christian Desrosiers,Ismail Ben Ayed,Gerard Sanroma,Oualid Benkarim,Adrià Casamitjana,Verónica Vilaplana,Weili Lin,Gang Li,Dinggang Shen +28 more
TL;DR: The iSeg-2017 challenge provides a set of six-month infant subjects with manual labels for training and testing the participating methods, and among the 21 automatic segmentation methods participating, the eight top-ranked teams are reviewed, in terms of Dice ratio, modified Hausdorff distance, and average surface distance.
Journal ArticleDOI
Hierarchical multi-atlas label fusion with multi-scale feature representation and label-specific patch partition.
TL;DR: The proposed hierarchical label fusion method with multi-scale feature representations and label-specific atlas patches are more accurate than several well-known state-of-the-art label fusion methods.
Journal ArticleDOI
Learning to Rank Atlases for Multiple-Atlas Segmentation
TL;DR: This work proposes a learning-based atlas selection method to pick up the best atlases that would lead to a more accurate segmentation, and shows the advantages of this method in an extensive experimental evaluation in the ADNI, SATA, IXI, and LONI LPBA40 datasets.
Book ChapterDOI
A Radiomics Approach to Computer-Aided Diagnosis with Cardiac Cine-MRI
Irem Cetin,Gerard Sanroma,Steffen E. Petersen,Sandy Napel,Oscar Camara,Miguel Ángel González Ballester,Miguel Ángel González Ballester,Karim Lekadir +7 more
TL;DR: In this article, the authors presented a new approach to identify cardiovascular diseases from cine-MRI by estimating large pools of radiomic features (statistical, shape and textural features) encoding relevant changes in anatomical and image characteristics due to CVDs.