# Promptable Longitudinal Lesion Segmentation in Whole-Body CT

## TL;DR

This paper introduces an extension to the LongiSeg framework that incorporates promptable capabilities for longitudinal lesion segmentation in whole-body CT scans, significantly improving lesion tracking accuracy through pretraining on synthetic data.

## Contribution

It presents a novel promptable extension to LongiSeg for lesion tracking, leveraging synthetic data pretraining to enhance longitudinal context utilization.

## Key findings

- Pretraining improves Dice scores by up to 6 points.
- Promptable LongiSeg effectively tracks lesions across time.
- Synthetic data pretraining enhances model performance.

## Abstract

Accurate segmentation of lesions in longitudinal whole-body CT is essential for monitoring disease progression and treatment response. While automated methods benefit from incorporating longitudinal information, they remain limited in their ability to consistently track individual lesions across time. Task 2 of the autoPET/CT IV Challenge addresses this by providing lesion localizations and baseline delineations, framing the problem as longitudinal promptable segmentation. In this work, we extend the recently proposed LongiSeg framework with promptable capabilities, enabling lesion-specific tracking through point and mask interactions. To address the limited size of the provided training set, we leverage large-scale pretraining on a synthetic longitudinal CT dataset. Our experiments show that pretraining substantially improves the ability to exploit longitudinal context, yielding an improvement of up to 6 Dice points compared to models trained from scratch. These findings demonstrate the effectiveness of combining longitudinal context with interactive prompting for robust lesion tracking. Code is publicly available at https://github.com/MIC-DKFZ/LongiSeg/tree/autoPET.