A voxel-level lesion segmentation dataset derived from the publicly available Breast Cancer Screening Digital Breast Tomosynthesis (BCS-DBT) dataset. doi-10-71775-kth-h54ts-b6y77-0 https://doi.org/10.71775/KTH.H54TS-B6Y77 Swedish National Data Service Svensk nationell datatjänst Landing page A voxel-level lesion segmentation dataset derived from the publicly available Breast Cancer Screening Digital Breast Tomosynthesis (BCS-DBT) dataset. doi-10-71775-kth-h54ts-b6y77-0 https://doi.org/10.71775/KTH.H54TS-B6Y77 101073222 Swedish National Data Service Svensk nationell datatjänst Landing page 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 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, BREAST-CANCER-SCREENING-DBT - The Cancer Imaging Archive (TCIA)) 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, Breast Imaging 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 the development of deep learning models for DBT image analysis.  A more detailed description and annotation protocol can be found at the preprint https://doi.org/10.21203/rs.3.rs-10669063/v1.  This repo provides processed DBT images, lesion segmentation files and structured BI-RADS information (label.zip). Additionally, we provided two trained segmentation nnUNet weights (model_weights.zip) for further validation using the  MIC-DKFZ/nnUNet code.    This work was supported by grants from Marie Skłodowska-Curie Doctoral Networks Actions (HORIZON-MSCA-2021-DN-01-01;  101073222) , Cancerfonden (22-2389 Pj) and Natural Science Foundation of Fujian Province, China (2024J011643, to Muzhen He). The computations were enabled by the Berzelius resource provided by the Knut and Alice Wallenberg Foundation at the National Academic Infrastructure for Supercomputing in Sweden. We also acknowledge the Cancer Imaging Archive team and BSC-DBT dataset owner for making the imaging and clinical data used in this study publicly available. Citation: Zhikai Yang, Qingxia Zheng, Jacob Schultzén, Zexuan Chen, Örjan Smedby, Muzhen He, Rodrigo Moreno BCSDBT-Seg Lesion Segmentation Annotations and Structured BI-RADS Reports for Breast Tomosynthesis Dataset, 11 August 2026, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-10669063/v1] Access to data through an external actor. Åtkomst till data via extern aktör.