Chen Chen

TL;DR: In this article, a review of deep learning-based segmentation methods for cardiac image segmentation is provided, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound.

TL;DR: In this article, a review of deep learning-based segmentation methods for cardiac image segmentation is provided, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound and major anatomical structures of interest (ventricles, atria and vessels).

TL;DR: This large-scale benchmarking study makes a significant step towards much-improved segmentation methods for atrial LGE-MRIs, and will serve as an important benchmark for evaluating and comparing the future works in the field.

TL;DR: A novel self-supervised FSS framework for medical images in order to eliminate the requirement for annotations during training, and superpixel-based pseudo-labels are generated to provide supervision.

TL;DR: In this article, a self-supervised feature learning method was proposed for cardiac MR image segmentation, in which features were learned in a selfsupervised manner by predicting anatomical positions.