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arXiv · 2601.02988

ULS+: Data-driven Model Adaptation Enhances Lesion Segmentation

Rianne Weber·Niels Rocholl·Max de Grauw·Mathias Prokop·Ewoud Smit·Alessa Hering
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Abstract

In this study, we present ULS+, an enhanced version of the Universal Lesion Segmentation (ULS) model. The original ULS model segments lesions across the whole body in CT scans given volumes of interest (VOIs) centered around a click-point. Since its release, several new public datasets have become available that can further improve model performance. ULS+ incorporates these additional datasets and uses smaller input image sizes, resulting in higher accuracy and faster inference. We compared ULS and ULS+ using the Dice score and robustness to click-point location on the ULS23 Challenge test data and a subset of the Longitudinal-CT dataset. In all comparisons, ULS+ significantly outperformed ULS. Additionally, ULS+ ranks first on the ULS23 Challenge test-phase leaderboard. By maintaining a cycle of data-driven updates and clinical validation, ULS+ establishes a foundation for robust and clinically relevant lesion segmentation models.

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Rianne Weber, Niels Rocholl, Max de Grauw, Mathias Prokop, Ewoud Smit, Alessa Hering. 2026-01-06. ULS+: Data-driven Model Adaptation Enhances Lesion Segmentation. https://arxiv.org/abs/2601.02988

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