基于解剖结构与交互提示的全身病灶分割模型，提升PET/CT图像分析精度。
Anatomy-Aware Promptable Segmentation with Online Interactive Training for AUTOPET V
- 采用双阶段训练：预训练生成初始分割，再通过笔画提示在线迭代优化。
- 解剖监督减少生理性摄取导致的假阳性，交叉验证中Dice分数持续提升。
- 支持多种示踪剂自动识别，适合临床医生实时交互式诊断使用。
我们提出一种解剖感知、可交互提示的全身体积病灶分割模型，用于FDG和PSMA PET/CT影像，专为AUTOPET V挑战设计。该方法基于nnU-Net构建，分两阶段训练：第一阶段为预训练，生成强初筛结果；第二阶段为在线交互训练，通过笔画提示逐步优化预测。通过共享头部同时预测病灶与器官，引入解剖上下文信息，有效降低生理摄取引发的假阳性。由于推理时示踪剂类型（FDG/PSMA）未知，模型采用基于图像处理与随机森林的冠状面最大强度投影特征分类器，将每例影像路由至联合模型或专用PSMA模型。四折交叉验证显示，解剖监督模型表现最佳且最稳定，交互阶段每次提示均使Dice分数单调提升，专用PSMA训练获得最优示踪剂特定性能。
原文摘要 · Abstract (English)
We present an anatomy-aware, promptable model for whole-body lesion segmentation in FDG and PSMA PET/CT, developed for the AUTOPET V challenge. The proposed method is built as family of nnU-Net-based models and trained in two stages: i) a pre-training stage that produces a strong initial segmentation, and ii) an online interactive stage that learns to exploit scribble prompts, refining the prediction over successive interactions. Anatomical context is incorporated through organ supervision using a single shared head that predicts lesions and organs from the same features, which reduces false positives arising from physiological uptake. Also as the tracer (i.e., FDG/PSMA) is not provided at inference, we add a tracer classifier based on image processing and a random forest over coronal MIP features, routing each study to a combined FDG+PSMA model or to a PSMA-specific model. Across four-fold cross-validation the organ-supervised model achieves the best and most stable performance, the interactive stage improves the Dice score monotonically with each prompt, and PSMA-specific training yields the strongest tracer-wise results.
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