Publications
Here is a list of my recent publications. You can find the complete list of my articles on
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Published in Medical Image Computing and Computer Assisted Intervention -- MICCAI, 2024
We present an innovative reinforcement learning framework for optimal domain adaptation in medical imaging segmentation. This framework reduces the need to otherwise incorporate large expertly annotated datasets in the target domain, and eliminates the need for lengthy manual human review.
Recommended citation: Judge, Arnaud (2024). "Domain Adaptation of Echocardiography Segmentation Via Reinforcement Learning." Medical Image Computing and Computer Assisted Intervention -- MICCAI. https://arxiv.org/pdf/2406.17902
Published in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2024
We propose novel alternatives to the traditional intraventricular vector flow mapping (iVFM) optimization scheme by utilizing physics-informed neural networks (PINNs) and a physics-guided nnU-Net-based supervised approach.
Recommended citation: Ling, Hang Jung (2024). "Physics-Guided Neural Networks for Intraventricular Vector Flow Mapping." IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control. https://arxiv.org/abs/2403.13040
Published in Medical Image Computing and Computer Assisted Intervention -- MICCAI, 2023
We propose explicitly modeling location uncertainty by redefining the segmentation task as contour regression, providing improved performance and interpretability.
Recommended citation: Judge, Thierry (2023). "Asymmetric Contour Uncertainty Estimation for Medical Image Segmentation." Medical Image Computing and Computer Assisted Intervention -- MICCAI. https://hal.science/hal-04243975v1
Published in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2023
We developped a complex-weighted convolutional neural network (CNN) for ultrasound images. We showed that our method allows reconstructing high-quality static images while maintaining the capability of tracking cardiac motion
Recommended citation: Lu, Jingfeng (2023). "Ultrafast Cardiac Imaging Using Deep Learning For Speckle-Tracking Echocardiography." IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control. https://arxiv.org/abs/2306.14265
Published in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2023
We developed an unfolded primal-dual network to unwrap (dealias) color Doppler echocardiographic images and compared its effectiveness against two state-of-the-art segmentation approaches based on nnU-Net and transformer models.
Recommended citation: Ling, Hang Jung (2023). "Phase unwrapping of color Doppler echocardiography using deep learning." IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control. 70(8). https://hal.science/hal-04142824v2
Published in IEEE Transactions on Medical Imaging, 2022
We propose a framework to learn the 2D+time apical long-axis cardiac shape such that the segmented sequences can benefit from temporal and anatomical consistency constraints.
Recommended citation: Painchaud, Nathan (2022). "Echocardiography Segmentation With Enforced Temporal Consistency." IEEE Transactions on Medical Imaging. 41(10). https://hal.science/hal-03672999
Published in Medical Image Computing and Computer Assisted Intervention -- MICCAI, 2022
We propose a method called CRISP for uncertainty prediction of image segmentation. CRISP implements a contrastive method to learn a joint latent space which encodes a distribution of valid segmentations and their corresponding images.
Recommended citation: Judge, Thierry (2022). "CRISP - Reliable Uncertainty Estimation for Medical Image Segmentation." Medical Image Computing and Computer Assisted Intervention -- MICCAI. https://hal.science/hal-04215854
Published in IEEE Transactions on Medical Imaging, 2022
We propose a novel deep learning solution for motion estimation in echocardiography. In parallel, we designed a novel simulation pipeline allowing the generation of a large amount of realistic B-mode sequences.
Recommended citation: Evain, Ewan (2022). "Motion Estimation by Deep Learning in 2D Echocardiography: Synthetic Dataset and Validation." IEEE Transactions on Medical Imaging. 41(8). https://hal.science/hal-03603014
Published in medRxiv, 2022
We evaluate the accuracy and reproducibility of 2D echocardiography left ventricular volumes and ejection fraction estimates by Deep Learning versus manual contouring and against CMR.
Recommended citation: Saloux, Eric (2022). "Deep Learning vs manual techniques for assessing left ventricular ejection fraction in 2D echocardiography: validation against CMR." medRxiv. https://www.medrxiv.org/content/10.1101/2022.07.26.22278059v1
Published in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2022
We present a numerical framework for generating clinical-like Color Doppler imaging. Synthetic blood vector fields were obtained from a patient-specific computational fluid dynamics CFD model.
Recommended citation: Sun, Yunyun (2022). "A Pipeline for the Generation of Synthetic Cardiac Color Doppler." IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control. 69(3). https://cnrs.hal.science/hal-03538666
Published in Plos one, 2022
This paper introduces CMRSegTools: an open-source application software designed for the segmentation and quantification of myocardial infarct lesion enabling full access to state-of-the-art segmentation methods and parameters, easy integration of new algorithms and standardised results sharing.
Recommended citation: Romero, William (2022). "CMRSegTools: An open-source software enabling reproducible research in segmentation of acute myocardial infarct in CMR images." Plos one. 17(9). https://hal.science/hal-04215855
