A Semi-Supervised Approach with Error Reflection for Echocardiography Segmentation

作     者：Han, Xiaoxiang Liu, Yiman Shang, Jiang Li, Qingli Chen, Jiangang Hu, Menghan Zhang, Qi Zhang, Yuqi Wang, Yan 

作者机构：School of Communication and Information Engineering Shanghai University Shanghai China Lab Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China Department of Pediatric Cardiology Shanghai Children’s Medical Center School of Medicine Shanghai Jiao Tong University Shanghai China  Shanghai China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Medical imaging 

摘      要：Segmenting internal structure from echocardiography is essential for the diagnosis and treatment of various heart diseases. Semi-supervised learning shows its ability in alleviating annotations scarcity. While existing semi-supervised methods have been successful in image segmentation across various medical imaging modalities, few have attempted to design methods specifically addressing the challenges posed by the poor contrast, blurred edge details and noise of echocardiography. These characteristics pose challenges to the generation of high-quality pseudo-labels in semi-supervised segmentation based on Mean Teacher. Inspired by human reflection on erroneous practices, we devise an error reflection strategy for echocardiography semi-supervised segmentation architecture. The process triggers the model to reflect on inaccuracies in unlabeled image segmentation, thereby enhancing the robustness of pseudo-label generation. Specifically, the strategy is divided into two steps. The first step is called reconstruction reflection. The network is tasked with reconstructing authentic proxy images from the semantic masks of unlabeled images and their auxiliary sketches, while maximizing the structural similarity between the original inputs and the proxies. The second step is called guidance correction. Reconstruction error maps decouple unreliable segmentation regions. Then, reliable data that are more likely to occur near high-density areas are leveraged to guide the optimization of unreliable data potentially located around decision boundaries. Additionally, we introduce an effective data augmentation strategy, termed as multi-scale mixing up strategy, to minimize the empirical distribution gap between labeled and unlabeled images and perceive diverse scales of cardiac anatomical structures. Extensive experiments on a public echocardiography dataset CAMUS, and a private clinical echocardiography dataset demonstrate the competitiveness of the proposed method. Copyright © 202