CT-Lesion: A Multi-Region CT Dataset for Co-existing Lesion Segmentation and Detection

Abstract

Lesion analysis in CT imaging, the task of identifying and delineating abnormal regions across diverse anatomical sites, plays a fundamental role in clinical diagnosis. Existing CT lesion datasets typically focus on a single disease type within a specific region. This design leads to two main limitations. First, co-existing lesions outside the predefined categories are left unannotated, so models may treat real lesions as background during training. Second, lesion categories within any single region follow a long-tailed distribution, in which rare but clinically important categories are underrepresented. However, collecting enough samples for every rare category within a single region is often impractical. We observe that certain lesion categories are rare in one region but occur more frequently in another, motivating the use of cross-region complementarity for lesion detection. In this work, we curate a multi-region CT lesion dataset, namely CT-Lesion, which covers brain, chest, and abdomen CT images. Our CT-Lesion is sourced from a private cohort of 500 clinical CT scans. Seven radiologists spent approximately 1,600 working hours delineating lesions of any type, rather than restricting annotations to a predefined set of categories. As a result, CT-Lesion provides clinically confirmed voxel-level segmentation masks and 28 fine-grained lesion labels. Moreover, we further group the lesions into 9 coarse categories to alleviate data scarcity issues. To demonstrate the effectiveness and utility of CT-Lesion, we benchmark the representative methods on CT-Lesion for lesion segmentation and detection. Furthermore, pretraining on CT-Lesion improves model performance on external benchmarks, including LiTS, KiTS, and LIDC-IDRI, demonstrating its potential as a transferable resource for CT lesion analysis. Our dataset is available at Kaggle.