针对肝脏血管多尺度特性，提出分层对比学习方法提升分割精度。
Scale-specific auxiliary multi-task contrastive learning for deep liver vessel segmentation
- 按血管尺度分层，设计特定辅助任务与对比学习
- 在3D-IRCADb数据集上达到新最佳性能
- 适合医学图像分割与多尺度特征学习研究者
从腹部影像中提取肝血管对临床划分柯尼奥德功能区具有重要意义。尽管语义分割方法性能不断提升，但保持主血管与分支复杂的多尺度几何结构仍是重大挑战。本文提出一种新的深度监督方法，重点捕捉血管树几何固有的多尺度表征。提出一种新聚类技术，将血管树分解为从微小到大型的多个尺度层级。在此基础上，将标准3D UNet扩展为多任务学习框架，引入尺度特异性辅助任务与对比学习，以增强共享表征中不同尺度间的区分能力。在公开的3D-IRCADb数据集上，多种评估指标均展现出显著提升结果。
原文摘要 · Abstract (English)
Extracting hepatic vessels from abdominal images is of high interest for clinicians since it allows to divide the liver into functionally-independent Couinaud segments. In this respect, an automated liver blood vessel extraction is widely summoned. Despite the significant growth in performance of semantic segmentation methodologies, preserving the complex multi-scale geometry of main vessels and ramifications remains a major challenge. This paper provides a new deep supervised approach for vessel segmentation, with a strong focus on representations arising from the different scales inherent to the vascular tree geometry. In particular, we propose a new clustering technique to decompose the tree into various scale levels, from tiny to large vessels. Then, we extend standard 3D UNet to multi-task learning by incorporating scale-specific auxiliary tasks and contrastive learning to encourage the discrimination between scales in the shared representation. Promising results, depicted in several evaluation metrics, are revealed on the public 3D-IRCADb dataset.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。