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 left atrial 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.