ULS+通过数据驱动优化，提升全身病灶分割精度与速度。
ULS+: Data-driven Model Adaptation Enhances Lesion Segmentation
- 融合新公开数据集，采用更小输入尺寸提升效率
- 在ULS23挑战赛中Dice分数显著超越原版模型
- 适合临床病灶分割需求，支持持续迭代更新
本研究提出ULS+，是通用病灶分割（ULS）模型的增强版本。原始ULS模型基于点击点周围感兴趣体积（VOIs），在CT扫描中实现全身病灶分割。自发布以来，多个新公开数据集可用，可进一步提升性能。ULS+整合这些数据并使用更小输入图像尺寸，实现更高精度和更快推理速度。我们在ULS23挑战赛测试数据及纵向CT数据子集上对比了ULS与ULS+，结果表明：在所有评估中，ULS+均显著优于原模型。此外，ULS+在ULS23挑战赛测试阶段排行榜上排名第一。通过持续的数据驱动更新与临床验证，ULS+为构建鲁棒且临床相关的病灶分割模型奠定基础。
原文摘要 · Abstract (English)
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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