Unstable Prompts, Unreliable Segmentations: A Challenge for Longitudinal Lesion Analysis
This addresses a critical problem in oncological care by highlighting limitations in automated tools for longitudinal lesion tracking, though it is incremental as it builds on existing segmentation models.
The paper investigated the ULS23 segmentation model's performance in longitudinal lesion analysis, finding that segmentation accuracy collapses due to registration errors and lesion displacement, with a sharp degradation in follow-up cases. It concludes that robust tracking requires integrated, end-to-end models for temporal analysis.
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.