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.

翻译：本研究提出ULS+，即通用病灶分割（ULS）模型的增强版本。原始ULS模型能够在CT扫描中，以点击点为中心的兴趣区域（VOI）为输入，实现全身病灶的分割。自发布以来，多个新公开数据集的可用性为模型性能的进一步提升提供了可能。ULS+整合了这些新增数据集，并采用更小的输入图像尺寸，从而实现了更高的精度与更快的推理速度。我们在ULS23挑战赛测试数据及Longitudinal-CT数据集的子集上，通过Dice分数和对点击点位置的鲁棒性对ULS与ULS+进行了比较。在所有对比中，ULS+均显著优于ULS。此外，ULS+在ULS23挑战赛测试阶段排行榜上位列第一。通过保持数据驱动更新与临床验证的循环，ULS+为构建鲁棒且具有临床相关性的病灶分割模型奠定了基础。