AUTOPET V挑战的新模型，结合解剖知识和交互式训练，能更准确分割PET/CT中的病变。
研究人员为AUTOPET V挑战开发了基于nnU-Net的全身病变分割模型。该模型采用两阶段训练：预训练产生初始分割结果，在线交互阶段学习利用涂鸦提示逐步优化预测。通过器官监督减少生理摄取导致的假阳性，并添加示踪剂分类器处理FDG和PSMA两种示踪剂。四折交叉验证显示器官监督模型性能最佳且稳定，交互阶段每次提示都能提高Dice分数。
Anatomy-Aware Promptable Segmentation with Online Interactive Training for AUTOPET V
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.