# Towards Interactive Lesion Segmentation in Whole-Body PET/CT with Promptable Models

## TL;DR

This paper introduces promptable models for interactive lesion segmentation in whole-body PET/CT scans, enhancing accuracy and efficiency by incorporating user prompts and robust training strategies.

## Contribution

It extends the autoPET/CT nnU-Net framework with promptable capabilities using EDT encodings and online user interaction simulation, improving segmentation performance.

## Key findings

- EDT encodings outperform Gaussian kernels for spatial prompts.
- Ensemble models trained with external data achieve top cross-validation results.
- Promptable models reduce false positives and negatives in lesion segmentation.

## Abstract

Whole-body PET/CT is a cornerstone of oncological imaging, yet accurate lesion segmentation remains challenging due to tracer heterogeneity, physiological uptake, and multi-center variability. While fully automated methods have advanced substantially, clinical practice benefits from approaches that keep humans in the loop to efficiently refine predicted masks. The autoPET/CT IV challenge addresses this need by introducing interactive segmentation tasks based on simulated user prompts. In this work, we present our submission to Task 1. Building on the winning autoPET III nnU-Net pipeline, we extend the framework with promptable capabilities by encoding user-provided foreground and background clicks as additional input channels. We systematically investigate representations for spatial prompts and demonstrate that Euclidean Distance Transform (EDT) encodings consistently outperform Gaussian kernels. Furthermore, we propose online simulation of user interactions and a custom point sampling strategy to improve robustness under realistic prompting conditions. Our ensemble of EDT-based models, trained with and without external data, achieves the strongest cross-validation performance, reducing both false positives and false negatives compared to baseline models. These results highlight the potential of promptable models to enable efficient, user-guided segmentation workflows in multi-tracer, multi-center PET/CT. Code is publicly available at https://github.com/MIC-DKFZ/autoPET-interactive