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

arXiv: https://arxiv.org/abs/2608.28461 · HTML: https://mlsanity.com/paper/2608.28461/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 11 references that are in the map.
- First posted on arXiv 28 Aug 2026.

## Builds on (most cited first, 5 of 5)

- [TotalSegmentator: robust segmentation of 104 anatomical structures in CT images](https://mlsanity.com/paper/2208.05868/) (2022)
- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [Autopet III challenge: Incorporating anatomical knowledge into nnUNet for lesion segmentation in PET/CT](https://mlsanity.com/paper/2409.12155/) (2024)
- [Towards Interactive Lesion Segmentation in Whole-Body PET/CT with Promptable Models](https://mlsanity.com/paper/2508.21680/) (2025)
- [autoPET IV challenge: Incorporating organ supervision and human guidance for lesion segmentation in PET/CT](https://mlsanity.com/paper/2509.02402/) (2025)

## Abstract (arXiv)

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.