将器官监督与人工引导融入PET/CT病灶分割，提升自动化精度。
autoPET IV challenge: Incorporating organ supervision and human guidance for lesion segmentation in PET/CT
- 结合追踪剂分类与器官标注，增强模型先验知识
- 零引导下实现稳健分割，交互后精度持续提升
- 适合医学影像算法研发与临床辅助诊断场景
PET/CT中的病灶分割是现代肿瘤学工作流的关键环节。为解决手动标注耗时长、观察者间差异大的问题，autoPET挑战系列致力于推进复杂多追踪剂、多中心环境下的自动化分割方法。在此基础上，autoPET IV引入人机协同场景，高效利用交互式人工指导。本文将追踪剂分类、器官监督与模拟点击引导集成至nnUNet残差编码器框架，构建一体化流程，在完全自动（零引导）条件下表现稳健，并通过迭代交互逐步提升分割精度。
原文摘要 · Abstract (English)
Lesion Segmentation in PET/CT scans is an essential part of modern oncological workflows. To address the challenges of time-intensive manual annotation and high inter-observer variability, the autoPET challenge series seeks to advance automated segmentation methods in complex multi-tracer and multi-center settings. Building on this foundation, autoPET IV introduces a human-in-the-loop scenario to efficiently utilize interactive human guidance in segmentation tasks. In this work, we incorporated tracer classification, organ supervision and simulated clicks guidance into the nnUNet Residual Encoder framework, forming an integrated pipeline that demonstrates robust performance in a fully automated (zero-guidance) context and efficiently leverages iterative interactions to progressively enhance segmentation accuracy.
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