Unstable Prompts, Unreliable Segmentations: A Challenge for Longitudinal Lesion Analysis

TL;DR

This paper highlights the limitations of current lesion segmentation models in longitudinal analysis, demonstrating their vulnerability to registration errors and lesion displacement, and advocates for integrated models designed for temporal consistency.

Contribution

It systematically evaluates the ULS23 segmentation model in longitudinal settings, revealing critical failure modes and proposing a paradigm shift towards end-to-end temporal models.

Findings

Segmentation quality degrades with inter-scan registration errors.

Model performance collapses when lesions are displaced from the center.

Current single-timepoint models are fundamentally limited for longitudinal tracking.

Abstract

Longitudinal lesion analysis is crucial for oncological care, yet automated tools often struggle with temporal consistency. While universal lesion segmentation models have advanced, they are typically designed for single time points. This paper investigates the performance of the ULS23 segmentation model in a longitudinal context. Using a public clinical dataset of baseline and follow-up CT scans, we evaluated the model's ability to segment and track lesions over time. We identified two critical, interconnected failure modes: a sharp degradation in segmentation quality in follow-up cases due to inter-scan registration errors, and a subsequent breakdown of the lesion correspondence process. To systematically probe this vulnerability, we conducted a controlled experiment where we artificially displaced the input volume relative to the true lesion center. Our results demonstrate that the model's performance is highly dependent on its assumption of a centered lesion; segmentation accuracy collapses when the lesion is sufficiently displaced. These findings reveal a fundamental limitation of applying single-timepoint models to longitudinal data. We conclude that robust oncological tracking requires a paradigm shift away from cascading single-purpose tools towards integrated, end-to-end models inherently designed for temporal analysis.