126 citations · 392 across the 21 of their papers we have counts for
14 papers · 1 filter
Computer-aided shape features extraction and regression models for predicting the ascending aortic aneurysm growth rate
Leonardo Geronzi, Antonio Martinez, Michel Rochette +13
Objective: ascending aortic aneurysm growth prediction is still challenging in clinics. In this study, we evaluate and compare the ability of local and global shape features to pre…
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…
Deep Learning Based Cardiac MRI Segmentation: Do We Need Experts?
Youssef Skandarani, Pierre-Marc Jodoin, Alain Lalande
Deep learning methods are the de-facto solutions to a multitude of medical image analysis tasks. Cardiac MRI segmentation is one such application which, like many others, requires…
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…
Segmentation of the Myocardium on Late-Gadolinium Enhanced MRI based on 2.5 D Residual Squeeze and Excitation Deep Learning Model
Abdul Qayyum, Alain Lalande, Thomas Decourselle +3
Cardiac left ventricular (LV) segmentation from short-axis MRI acquired 10 minutes after the injection of a contrast agent (LGE-MRI) is a necessary step in the processing allowing…
On the effectiveness of GAN generated cardiac MRIs for segmentation
Youssef Skandarani, Nathan Painchaud, Pierre-Marc Jodoin +1
In this work, we propose a Variational Autoencoder (VAE) - Generative Adversarial Networks (GAN) model that can produce highly realistic MRI together with its pixel accurate ground…
Deep generative model-driven multimodal prostate segmentation in radiotherapy
Kibrom Berihu Girum, Gilles Créhange, Raabid Hussain +2
Deep learning has shown unprecedented success in a variety of applications, such as computer vision and medical image analysis. However, there is still potential to improve segment…
3D landmark detection for augmented reality based otologic procedures
Raabid Hussain, Alain Lalande, Kibrom Berihu Girum +2
Ear consists of the smallest bones in the human body and does not contain significant amount of distinct landmark points that may be used to register a preoperative CT-scan with th…
Cardiac MRI Segmentation with Strong Anatomical Guarantees
Nathan Painchaud, Youssef Skandarani, Thierry Judge +3
Recent publications have shown that the segmentation accuracy of modern-day convolutional neural networks (CNN) applied on cardiac MRI can reach the inter-expert variability, a gre…
Sigmoid function based intensity transformation for parameter initialization in MRI-PET Registration Tool for Preclinical Studies
Hiliwi Leake Kidane, Stephanie Bricq, Alain Lalande
Images from Positron Emission Tomography (PET) deliver functional data such as perfusion and metabolism. On the other hand, images from Magnetic Resonance Imaging (MRI) provide inf…