126 citations · 188 across the 10 of their papers we have counts for
12 papers
Myocardial Strain Drift Correction in Deep Learning Based Ultrasound Tracking
Thierry Judge, Nicolas Duchateau, Andreas Østvik +6
Myocardial strain from echocardiography is a key biomarker for cardiac function. Recent deep learning methods show strong performance for myocardial motion tracking but often lack…
Deep Learning Strain Estimation: Is Physics-Based Simulation the Solution?
Thierry Judge, Nicolas Duchateau, Andreas Østvik +13
Speckle tracking echocardiography (STE) is the clinical standard for myocardial strain estimation. Despite good performance on global strain (GLS), its accuracy for regional strain…
Estimation of Segmental Longitudinal Strain in Transesophageal Echocardiography by Deep Learning
Anders Austlid Taskén, Thierry Judge, Erik Andreas Rye Berg +8
Segmental longitudinal strain (SLS) of the left ventricle (LV) is an important prognostic indicator for evaluating regional LV dysfunction, in particular for diagnosing and managin…
Reinforcement Learning for Unsupervised Domain Adaptation in Spatio-Temporal Echocardiography Segmentation
Arnaud Judge, Nicolas Duchateau, Thierry Judge +5
Domain adaptation methods aim to bridge the gap between datasets by enabling knowledge transfer across domains, reducing the need for additional expert annotations. However, many a…
Generation of realistic cardiac ultrasound sequences with ground truth motion and speckle decorrelation
Thierry Judge, Nicolas Duchateau, Khuram Faraz +2
Simulated ultrasound image sequences are key for training and validating machine learning algorithms for left ventricular strain estimation. Several simulation pipelines have been…
Uncertainty Propagation for Echocardiography Clinical Metric Estimation via Contour Sampling
Thierry Judge, Olivier Bernard, Woo-Jin Cho Kim +5
Echocardiography plays a fundamental role in the extraction of clinical parameters (e.g. left ventricular volume and ejection fraction) required to determine the presence and sever…
Domain Adaptation of Echocardiography Segmentation Via Reinforcement Learning
Arnaud Judge, Thierry Judge, Nicolas Duchateau +4
Performance of deep learning segmentation models is significantly challenged in its transferability across different medical imaging domains, particularly when aiming to adapt thes…
Left Ventricle Contouring of Apical Three-Chamber Views on 2D Echocardiography
Alberto Gomez, Mihaela Porumb, Angela Mumith +5
We propose a new method to automatically contour the left ventricle on 2D echocardiographic images. Unlike most existing segmentation methods, which are based on predicting segment…
CRISP - Reliable Uncertainty Estimation for Medical Image Segmentation
Thierry Judge, Olivier Bernard, Mihaela Porumb +3
Accurate uncertainty estimation is a critical need for the medical imaging community. A variety of methods have been proposed, all direct extensions of classification uncertainty e…
Neural Teleportation
Marco Armenta, Thierry Judge, Nathan Painchaud +5
In this paper, we explore a process called neural teleportation, a mathematical consequence of applying quiver representation theory to neural networks. Neural teleportation "telep…
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…
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…