https://researchdata.se/en/catalogue Search results 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 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] Wed, 22 Jul 2026 00:00:00 GMT https://researchdata.se/en/catalogue/dataset/oai-datarepository-kth-se-h54ts-b6y77 https://researchdata.se/en/catalogue/dataset/oai-datarepository-kth-se-h54ts-b6y77 Royal Institute of Technology Yang, Zhikai Breast Ultrasound (BUS) imaging plays an important role in the early detection and diagnosis of breast cancer. The rapid advancement of deep learning-based Computer-Aided Diagnosis (CAD) systems has demonstrated remarkable potential. However, the clinical applicability of CAD is constrained by the scarcity of large-scale, diverse, and well-annotated datasets. Existing public BUS datasets are highly fragmented. To address these gaps, we present BUS-Large, a large-scale curated BUS dataset for method development and evaluation, comprising more than 21,00 images from 21 publicly available datasets across multiple countries and clinical institutions. The dataset integrates all the public available static image datasets. All cohorts have been harmonized with unified diagnostic labels, pixel-level segmentation mask, and standardized BI-RADS clinical information. To the best of our knowledge, this is one of the most extensive datasets providing lesion segmentation masks annotated from publicly available BUS datasets.  This work was supported by grants from Marie Skłodowska-Curie Doctoral Networks Actions (HORIZON-MSCA-2021-DN-01-01;  101073222), Cancerfonden (22-2389 Pj). In addition to the MAIA platform at KTH, 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 public dataset owners for making the imaging and clinical data used in this study publicly available. Wed, 15 Jul 2026 00:00:00 GMT https://researchdata.se/en/catalogue/dataset/oai-datarepository-kth-se-frfd6-ymw69 https://researchdata.se/en/catalogue/dataset/oai-datarepository-kth-se-frfd6-ymw69 Royal Institute of Technology Yang, Zhikai This data contains the digital elevation models and polyline shapefiles with the location of channels from the 12 study areas used in this study. It also has the code to generate the datasets used to train the deep learning models to detect channels, ditches, and streams, and calculate the topographic indices. The code to train the models is also included, along with the models with the highest performance in 0.5 m resolution. The channels were mapped differently based on their type: ditches were manually digitized based on the visual analysis of some topographic indices and orthophotos obtained from the DEM. Streams were mapped by initially detecting all natural channel heads, then tracing the downstream channels, and finally manually editing them based on orthophotos. Fri, 15 Mar 2024 15:10:56 GMT https://researchdata.se/en/catalogue/dataset/2024-57 https://researchdata.se/en/catalogue/dataset/2024-57 Swedish University of Agricultural Sciences Mariana dos Santos Toledo Busarello William Lidberg Anneli Ågren Florian Westphal Assessment of left atrial (LA) fibrosis from late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) adds to the management of patients with atrial fibrillation (AF). However, accurate assessment of fibrosis in the LA wall remains challenging. Excluding anatomical structures in the LA proximity using clipping techniques can reduce misclassification of LA fibrosis. A novel FK-means approach for combined automatic clipping and automatic fibrosis segmentation was developed. This approach combines a feature-based Voronoi diagram with a hierarchical 3D K-means fractal-based method. The proposed automatic Voronoi clipping method was applied on LGE MRI data and achieved a Dice score of 0.75, similar as the score obtained by a deep learning method (3D UNet) for clipping (0.74). The automatic fibrosis segmentation method, which utilizes the Voronoi clipping method, achieved a Dice score of 0.76. This outperformed a 3D U-Net method for clipping and fibrosis classification, which had a Dice score of 0.69. Moreover, the proposed automatic fibrosis segmentation method achieved a Dice score of 0.90, using manual clipping of anatomical structures. The findings suggest that the automatic FK-means analysis approach enables reliable LA fibrosis segmentation and that clipping of anatomical structures in the atrial proximity can add to the assessment of atrial fibrosis. For access to data and code please contact biblioteket@liu.se for further information. The dataset was originally published in DiVA and moved to SND in 2024. Wed, 08 Nov 2023 00:00:00 GMT https://researchdata.se/en/catalogue/dataset/2024-401 https://researchdata.se/en/catalogue/dataset/2024-401 Linköping University Marjan Firouznia Markus Henningsson Carl-Johan Carlhäll The CSAW-S is a curated dataset containing mammography images with annotations of breast cancer and breast anatomy from experts and non-experts. It contains mammography screenings from 172 different patients with annotations for semantic segmentation. Additional information about our paper and the source code can be found here. If you use this dataset please cite our work Adding seemingly uninformative labels helps in low data regimes. Wed, 16 Sep 2020 00:00:00 GMT https://researchdata.se/en/catalogue/dataset/doi-10-5281-zenodo-4030660 https://researchdata.se/en/catalogue/dataset/doi-10-5281-zenodo-4030660 Royal Institute of Technology Matsoukas, Christos Hernandez, Albert Bou I Liu, Yue Dembrower, Karin Miranda, Gisele Konuk, Emir Fredin Haslum, Johan Zouzos, Athanasios Lindholm, Peter Strand, Fredrik Smith, Kevin 5 bone segmentation masks and 15 annotations of anatomical landmarks for pelvis bones in each of 90 Computed Tomography (CT) cases extracted from the [CT Lymph nodes](https://wiki.cancerimagingarchive.net/display/Public/CT+Lymph+Nodes) and [CT Colonography](https://wiki.cancerimagingarchive.net/display/Public/CT+COLONOGRAPHY) collections from the [The Cancer Imaging Archive](https://www.cancerimagingarchive.net/) (TCIA). Fri, 03 May 2019 00:00:00 GMT https://researchdata.se/en/catalogue/dataset/doi-10-23698-aida-ctpel https://researchdata.se/en/catalogue/dataset/doi-10-23698-aida-ctpel AIDA Data Hub Connolly, Bryan Wang, Chunliang