Two-stage Left Atrial Scar Segmentation Based on Multi-scale Attention and Uncertainty Loss

Atrial Fibrillation (AF) is one of the most common arrhythmias clinically.Accurate segmentation and area assessment of the left atrium and its scar area after myocardial infarction are of great clinical significance for the early diagnosis,treatment planning and prognosis assessment of AF in patients with myocardial infarction.The deep learning-based method is the mainstream direction for automatic segmentation of the left atrium and the scar area after myocardial infarction.However,as the scar after myocardial infarction is small in size and easily affected by the surrounding enhanced tissue,the segmentation accuracy still remains to be improved.Therefore,a two-stage deep learning model based on multi-scale attention and uncertainty loss is proposed.On the one hand,a Multi-Scale Attention Module (MSAM) is introduced before sampling on the network.This module can encode rich multi-scale semantic information and make the model pay more attention to important semantic and spatial information.On the other hand,uncertainty loss is introduced to enhance the model's ability to model scar uncertainty.In addition,this study also uses histogram matching (HM) to enhance image quality and improve the segmentation ability of the network.The proposed methodis verified on the validation set and the left atrial and scar quantification and segmentation (LAScarQS++) evaluation platform.The experimental results show that the scar segmented by this method is more complete and the segmentation accuracy is also improved.Compared with nnU-Net,the Dice coefficient (Dice) of scar segmentation after myocardial infarction is increased by 8.12%.