Anatomy-Aware Promptable Segmentation with Online Interactive Training for AUTOPET V

• URL: http://arxiv.org/abs/2608.28461v1
• Date: Fri, 28 Aug 2026 15:49:58 GMT
• Title: Anatomy-Aware Promptable Segmentation with Online Interactive Training for AUTOPET V
• Abstract summary: We present an anatomy-aware, promptable model for whole-body lesion segmentation in FDG and PSMA PET/CT.<n>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.
• Score: 7.780663270921085
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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