针对不同示踪剂的自动PET图像病灶分割，提出可交互的全流程解决方案。
Pretrained, Curriculum-Tuned, and Ensembled: A Tracer-Aware Interactive Segmentation Pipeline for AutoPET V
- 用异步掩码预训练3D STU-Net，学习跨模态解剖特征
- 通过器官分割提供解剖上下文，区分生理摄取与病灶
- 支持示踪剂自适应分支，适合医学影像交互分割场景
全身体积PET/CT中的交互式病灶分割要求模型在推理时既能生成强初始预测，又能高效响应稀疏修正笔画。该任务极具挑战性，因氟代脱氧葡萄糖（FDG）和前列腺特异性膜抗原（PSMA）研究中示踪剂分布、生理摄取模式、病灶外观及采集特性差异显著。本文提出TRIAGE：一种基于解剖引导的示踪剂感知交互分割流程。核心骨干为3D STU-Net，通过异步掩码策略进行掩码自编码预训练，以在特定任务微调前学习可迁移的解剖与跨模态表征。同时训练一个辅助器官分割模型，其输出提供明确解剖上下文，辅助区分生理摄取与恶性病灶。专用示踪剂分类器将每例研究路由至对应FDG或PSMA专用分支。各分支内，第一阶段分割模型融合CT、PET与器官上下文生成初始病灶掩码；随后结合累积的前景/背景笔画，由第二阶段交互分割网络进行精修。两个分支共享相同处理流程但独立训练，以适配示踪剂特异性外观与错误模式。此外，采用课程式训练与模型集成提升跨交互步骤与异质人群的鲁棒性。实验基于AutoPET V官方数据集与十折划分，定量结果、消融分析及最终测试集性能留待挑战评估后补全。
原文摘要 · Abstract (English)
Interactive lesion segmentation in whole-body PET/CT requires a model to provide a strong initial prediction while also responding efficiently to sparse corrective scribbles during inference. This setting is particularly challenging because tracer distributions, physiological uptake patterns, lesion appearance, and acquisition characteristics differ substantially between FDG and PSMA studies. We present TRIAGE, Tracer-aware Refinement via Interactive Anatomy-Guided sEgmentation. The core backbone is a 3D STU-Net initialized through masked autoencoding pre-training with an asynchronous masking strategy, aiming to learn transferable anatomical and cross-modal representations before task-specific fine-tuning. In parallel, we train an auxiliary organ segmentation model whose predictions provide explicit anatomical context and help distinguish physiological uptake from malignant lesions. A dedicated tracer classifier first routes each study to an FDG- or PSMA-specific branch. Within each branch, a first-stage segmentation model consumes CT, PET, and organ context to generate an initial lesion mask. The initial prediction is then combined with cumulative foreground/background scribbles and refined by a second interactive segmentation network. The FDG and PSMA branches share the same overall processing pipeline but are trained independently to account for tracer-specific appearance and error modes. We additionally employ curriculum-style training and model ensembling to improve robustness across interaction steps and heterogeneous cohorts. Experiments are conducted using the official AutoPET V data and ten-fold split; quantitative results, ablations, and final test-set performance are left as placeholders to be completed after the challenge evaluation. Code: https://github.com/Liiiii2101/AUTOPET2026-MEDAI.
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