发表机构
University of Amsterdam; BiometricsAI; Universidad Autónoma de Madrid（阿姆斯特丹大学; 生物识别AI公司; 马德里自治大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对AUTOPET V挑战，开发了基于nnU-Net的解剖感知可提示分割模型，经分阶段训练与示踪剂分类优化，在PET/CT病灶分割任务中实现了稳定且优异的性能。
AI 中文摘要
本文针对AUTOPET V挑战，提出了一种用于FDG和PSMA PET/CT全身病灶分割的解剖结构感知可提示模型。该方法基于nnU-Net系列模型构建，分两个阶段训练：i)预训练阶段生成初始强分割结果；ii)在线交互阶段学习利用 scribble 提示，通过多次交互迭代优化预测结果。模型通过单共享头整合解剖上下文，该头从同一特征中同时预测病灶与器官，减少生理摄取导致的假阳性。由于推理阶段未提供示踪剂（即FDG/PSMA），本文添加了基于图像处理的示踪剂分类器，以及基于冠状位MIP特征的随机森林分类器，将每个研究分配至FDG+PSMA组合模型或PSMA专用模型。四折交叉验证结果显示，器官监督模型取得最佳且最稳定的性能；交互阶段的Dice分数随每次提示单调提升；PSMA专用训练在按示踪剂划分的结果中表现最优。
英文摘要
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.
