Scholarlink

<p>In clinical practice, cardiologists require multi-view echocardiographic segmentation for accurate cardiovascular disease diagnosis. However, segmenting echocardiograms from multiple views presents significant challenges, including structural variations, a lack of manual annotations, and inconsistent acquisition methods. This study proposes an unsupervised domain adaptive approach based on semantic contrastive learning to overcome these challenges. Specifically, we apply a teacher-student architecture to knowledge transfer, implementing semantic alignment and pseudo-label generation to handle cross-domain semantic variations through two learning strategies. In knowledge transfer, Gaussian Projection Contrastive Learning enhances cross-domain category discrimination by establishing intrinsic connections between cardiac features across different viewing angles. For pseudo-label generation, a masked uncertainty-aware consistency learning strategy enforces consistency between the target view's cardiac appearance and the generated pseudo-labels, leveraging contextual reasoning to predict masked regions. Extensive evaluations were conducted on echocardiographic datasets comprising 688 patients from both internal and external sources. We designed not only inter-view transfer experiments to validate the performance of our method but also cross-dataset transfer experiments to further verify its generalizability across diverse adaptation scenarios.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Unsupervised domain adaptation for multi-view echocardiographic segmentation via gaussian contrast and masked consistency learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月6日

查看全文
  • Liekai Hong,
  • Jiahao Xu,
  • Shuxin Zhuang,
  • Jinhong Wang,
  • Shunmin Qiu,
  • Jingfeng Guo,
  • Zhemin Zhuang

摘要

In clinical practice, cardiologists require multi-view echocardiographic segmentation for accurate cardiovascular disease diagnosis. However, segmenting echocardiograms from multiple views presents significant challenges, including structural variations, a lack of manual annotations, and inconsistent acquisition methods. This study proposes an unsupervised domain adaptive approach based on semantic contrastive learning to overcome these challenges. Specifically, we apply a teacher-student architecture to knowledge transfer, implementing semantic alignment and pseudo-label generation to handle cross-domain semantic variations through two learning strategies. In knowledge transfer, Gaussian Projection Contrastive Learning enhances cross-domain category discrimination by establishing intrinsic connections between cardiac features across different viewing angles. For pseudo-label generation, a masked uncertainty-aware consistency learning strategy enforces consistency between the target view's cardiac appearance and the generated pseudo-labels, leveraging contextual reasoning to predict masked regions. Extensive evaluations were conducted on echocardiographic datasets comprising 688 patients from both internal and external sources. We designed not only inter-view transfer experiments to validate the performance of our method but also cross-dataset transfer experiments to further verify its generalizability across diverse adaptation scenarios.