Scholarlink

Accurate segmentation of thin tubular structures - e.g., vessels, nerves or roads - is crucial in computer vision. Standard segmentation loss functions, like dice or cross-entropy, focus on volumetric overlap, often neglecting structural connectivity or topology. This can lead to segmentation errors affecting tasks such as flow calculation, navigation, and structural inspection.

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

Abstract: Skeleton Recall Loss

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Yannick Kirchhoff,
  • Maximilian R. Rokuss,
  • Saikat Roy,
  • Balint Kovacs,
  • Constantin Ulrich,
  • Tassilo Wald,
  • Maximilian Zenk,
  • Philipp Vollmuth,
  • Jens Kleesiek,
  • Fabian Isensee,
  • Klaus Maier-Hein

摘要

Accurate segmentation of thin tubular structures - e.g., vessels, nerves or roads - is crucial in computer vision. Standard segmentation loss functions, like dice or cross-entropy, focus on volumetric overlap, often neglecting structural connectivity or topology. This can lead to segmentation errors affecting tasks such as flow calculation, navigation, and structural inspection.