126 citations · 230 across the 11 of their papers we have counts for
15 papers
Deep Learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge
Alain Lalande, Zhihao Chen, Thibaut Pommier +30
A key factor for assessing the state of the heart after myocardial infarction (MI) is to measure whether the myocardium segment is viable after reperfusion or revascularization the…
GANs for Medical Image Synthesis: An Empirical Study
Youssef Skandarani, Pierre-Marc Jodoin, Alain Lalande
Generative Adversarial Networks (GANs) have become increasingly powerful, generating mind-blowing photorealistic images that mimic the content of datasets they were trained to repl…
Learning With Context Feedback Loop for Robust Medical Image Segmentation
Kibrom Berihu Girum, Gilles Créhange, Alain Lalande
Deep learning has successfully been leveraged for medical image segmentation. It employs convolutional neural networks (CNN) to learn distinctive image features from a defined pixe…
A Mutual Reference Shape for Segmentation Fusion and Evaluation
S. Jehan-Besson, R. Clouard, C. Tilmant +7
This paper proposes the estimation of a mutual shape from a set of different segmentation results using both active contours and information theory. The mutual shape is here define…
Automatic Myocardial Infarction Evaluation from Delayed-Enhancement Cardiac MRI using Deep Convolutional Networks
Kibrom Berihu Girum, Youssef Skandarani, Raabid Hussain +3
In this paper, we propose a new deep learning framework for an automatic myocardial infarction evaluation from clinical information and delayed enhancement-MRI (DE-MRI). The propos…
Segmentation-free Estimation of Aortic Diameters from MRI Using Deep Learning
Axel Aguerreberry, Ezequiel de la Rosa, Alain Lalande +1
Accurate and reproducible measurements of the aortic diameters are crucial for the diagnosis of cardiovascular diseases and for therapeutic decision making. Currently, these measur…