Table of Contents

LASSNet: A four steps deep neural network for Left Atrial Segmentation and Scar Quantification.- Multi-Depth Boundary-Aware Left Atrial Scar Segmentation Network.- Self Pre-training with Single-scale Adapter for Left Atrial Segmentation.- UGformer for Robust Left Atrium and Scar Segmentation Across Scanners.- Automatically Segmenting the Left Atrium and Scars from LGE-MRIs Using a boundary-focused nnU-Net.- Two Stage of Histogram Matching Augmentation for Domain Generalization : Application to Left Atrial Segmentation .- Sequential Segmentation of the Left Atrium and Atrial Scars Using a Multi-scale Weight Sharing Network and Boundary-based Processing.- LA-HRNet: High-resolution network for automatic left atrial segmentation in multi-center LEG MRI .- Edge-enhanced Features Guided Joint Segmentation and Quantification of Left Atrium and Scars in LGE MRI Images.- TESSLA: Two-Stage Ensemble Scar Segmentation for the Left Atrium.- Deep U-Net architecture with curriculum learning for leftatrial segmentation.- Cross-domain Segmentation of Left Atrium Based on Multi-scale Decision Level Fusion.- Using Polynomial Loss and Uncertainty Information for Robust Left Atrial and Scar Quantification and Segmentation.- Automated segmentation of the left atrium and scar using deep convolutional neural networks.- Automatic Semi-Supervised Left Atrial Segmentation using Deep-Supervision 3DResUnet with Pseudo Labeling Approach for LAScarQS 2022 Challenge.