2026-10-14T21:07:44Z https://www.scidb.cn/oai

10.57760/sciencedb.42939 2026-07-16T09:35:55Z

2026-07-16 BCSDBT-seg doi:10.57760/sciencedb.42939 en Digital breast tomosynthesis (DBT) has become an important imaging modality for breast cancer screening because it reduces tissue overlap and improves lesion conspicuity compared with conventional mammography. Publicly available DBT datasets remain scarce, and existing datasets predominantly only provide lesion-level labels or bounding-box annotations. To facilitate the development and evaluation of segmentation algorithms for DBT, we present DBT-Seg dataset, a voxel-level lesion segmentation dataset derived from the publicly available Breast Cancer Screening Digital Breast Tomosynthesis (BCS-DBT) dataset. Expert annotators manually delineated lesion boundaries on DBT slices containing biopsy-proven benign and malignant findings. The dataset includes voxel-wise breast-lesion masks in standard image formats, linked to the original DBT examinations. In addition, BreastImaging Reporting and Data System (BI-RADS) scores are also provided per case. The dataset consists of 201 patients with 396 DBT images, including MLO and CC views with biopsy confirmation. The dataset is intended to support a broad range of applications, including lesion segmentation, radiomics, and development deep learning model for DBT image analysis.  Segmentation; Breast ; DBT Yang Zhikai RESTRICTED dataset Science Data Bank