126 citations · 126 across the 2 of their papers we have counts for
6 papers
Cardiac Segmentation with Strong Anatomical Guarantees
Nathan Painchaud, Youssef Skandarani, Thierry Judge +3
Convolutional neural networks (CNN) have had unprecedented success in medical imaging and, in particular, in medical image segmentation. However, despite the fact that segmentation…
LU-Net: a multi-task network to improve the robustness of segmentation of left ventriclular structures by deep learning in 2D echocardiography
Sarah Leclerc, Erik Smistad, Andreas Østvik +9
Segmentation of cardiac structures is one of the fundamental steps to estimate volumetric indices of the heart. This step is still performed semi-automatically in clinical routine,…
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…
Robustly segmenting quadriceps muscles of ultra-endurance athletes with weakly supervised U-Net
Hoai-Thu Nguyen, Pierre Croisille, Magalie Viallon +5
In this study, segmentation of quadriceps muscle heads of ultra-endurance athletes was done using a multi-atlas segmentation and corrective leaning framework where the registration…
Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography
Sarah Leclerc, Erik Smistad, João Pedrosa +11
Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the automation of this task has be…
Deep Learning Segmentation in 2D echocardiography using the CAMUS dataset : Automatic Assessment of the Anatomical Shape Validity
Sarah Leclerc, Erik Smistad, Andreas Østvik +9
We recently published a deep learning study on the potential of encoder-decoder networks for the segmentation of the 2D CAMUS ultrasound dataset. We propose in this abstract an ext…