Problem
Research questions and friction points this paper is trying to address.
lesion segmentation
anatomical context
physiological uptake
Anatomy-Aware Promptable Segmentation with Online Interactive Training for AUTOPET V
🤖 AI Summary
提出一种解剖感知、可提示的模型，用于全身病变分割，通过两阶段训练减少假阳性，并利用在线交互式学习和器官监督提高分割精度。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
anatomy-aware
promptable model
online interactive training
organ supervision
tracer classifier
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