M
Miguel Ángel González Ballester
Researcher at Pompeu Fabra University
Publications - 218
Citations - 4320
Miguel Ángel González Ballester is an academic researcher from Pompeu Fabra University. The author has contributed to research in topics: Computer science & Segmentation. The author has an hindex of 25, co-authored 194 publications receiving 2913 citations. Previous affiliations of Miguel Ángel González Ballester include T-Systems & Catalan Institution for Research and Advanced Studies.
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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
Estimation of the partial volume effect in MRI.
TL;DR: This paper provides a statistical estimation framework to quantify PVE and to propagate voxel-based estimates in order to compute global magnitudes, such as volume, with associated estimates of uncertainty.
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A 2D/3D correspondence building method for reconstruction of a patient-specific 3D bone surface model using point distribution models and calibrated X-ray images.
Guoyan Zheng,Sebastian Gollmer,Steffen Schumann,Xiao Dong,Thomas Feilkas,Miguel Ángel González Ballester +5 more
TL;DR: This paper presents a 2D/3D correspondence building method based on a non-rigid 2D point matching process, which iteratively uses a symmetric injective nearest-neighbor mapping operator and 2D thin-plate splines based deformations to find a fraction of best matched2D point pairs between features extracted from the X-ray images and those extracts from the 3D model.
Journal ArticleDOI
Statistical deformable bone models for robust 3D surface extrapolation from sparse data
Kumar T. Rajamani,Martin Styner,Haydar Talib,Guoyan Zheng,Lutz-Peter Nolte,Miguel Ángel González Ballester +5 more
TL;DR: This paper proposes a novel method to construct a patient-specific three-dimensional model that provides an appropriate intra-operative visualization without the need for a pre or intra-operatively imaging.
Journal ArticleDOI
Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CTA images using Deep Convolutional Neural Networks.
Karen López-Linares,Nerea Aranjuelo,Luis Kabongo,Gregory Maclair,Nerea Lete,Mario Ceresa,Ainhoa García-Familiar,Iván Macía,Miguel Ángel González Ballester +8 more
TL;DR: A new fully automatic approach based on Deep Convolutional Neural Networks (DCNN) for robust and reproducibleThrombus region of interest detection and subsequent fine thrombus segmentation and a new segmentation network architecture, based on Fully convolutional Networks and a Holistically‐Nested Edge Detection Network, is presented.