Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (6): 264-273.doi: 10.11896/jsjkx.241200197

• Computer Graphics & Multimedia • Previous Articles     Next Articles

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

ZHANG Xinyan1,2, TANG Zhenchao3,4, LI Yifu5, LIU Zhenyu1,2   

  1. 1 School of Public Health,Anhui Medical University,Hefei 230032,China
    2 CAS Key Laboratory of Molecular Imaging,Institute of Automation,Beijing 100190,China
    3 Beijing Advanced Innovation Center for Big Data-Based Precision Medicine,School of Engineering Medicine,Beihang University,Beijing 100191,China
    4 Key Laboratory of Big Data-Based Precision Medicine,Beihang University,Ministry of Industry and Information Technology,Beijing 100191,China
    5 National Superior College for Engineers,Beihang University,Beijing 100191,China
  • Received:2024-12-27 Revised:2025-03-18 Online:2025-06-15 Published:2025-06-11
  • About author:ZHANG Xinyan,born in 1996,postgraduate.Her main research interests include deep learning and medical image analysis.
    LIU Zhenyu,born in 1986,professor,Ph.D supervisor.His main research interests include artificial intelligence,pattern recognition,medical imaging big data analysis and multimodal medical image big models.
  • Supported by:
    Fundamental Research Funds for the Central Universities(YWF-23-Q-1074) and National Key R&D Program of China(2021YFA1301603).

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Abstract: 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%.

Key words: Post-Myocardial infarction scar, Deep learning, Image segmentation, Uncertainty loss, nnU-Net, Multi-scale attention

CLC Number: 

  • TP391.7
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