A dataset of mammography images with area-based breast density values, breast area, and dense tissue segmentation masks.

Hamid Behravan, Naga Raju Gudhe, Hidemi Okuma, Mazen Sudah, Arto Mannermaa

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Article in Data in brief, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 authors.

Hamid BehravanInstitute of Clinical Medicine, Pathology and Forensic Medicine, Multidisciplinary Cancer Research Community RC Cancer, University of Eastern Finland, P.O. Box 1627, 70211 Kuopio, Finland.

Naga Raju GudheInstitute of Clinical Medicine, Pathology and Forensic Medicine, Multidisciplinary Cancer Research Community RC Cancer, University of Eastern Finland, P.O. Box 1627, 70211 Kuopio, Finland.

Hidemi OkumaDepartment of Clinical Radiology, Kuopio University Hospital, P.O. Box 100, Kuopio 70029, Finland.

Mazen SudahDepartment of Clinical Radiology, Kuopio University Hospital, P.O. Box 100, Kuopio 70029, Finland.

Arto MannermaaInstitute of Clinical Medicine, Pathology and Forensic Medicine, Multidisciplinary Cancer Research Community RC Cancer, University of Eastern Finland, P.O. Box 1627, 70211 Kuopio, Finland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A new dataset is presented to propel research in automated breast density estimation, a crucial factor in mammogram interpretation. Mammography, a low-dose X-ray technique for breast cancer screening, can be affected by breast density. Dense tissue appears white on mammograms, potentially obscuring tumors. This dataset, built upon the public VinDr-Mammo dataset, offers 745 mammogram images (including training and test sets) along with expert-radiologist annotations for both the entire breast and dense tissue regions. Researchers can leverage this dataset for multiple purposes: training deep learning models for automated breast density analysis, refining segmentation methods for accurate delineation of breast tissue, and benchmarking existing and novel breast density estimation algorithms. This resource holds promise for improving breast cancer screening through advancements in automated breast density analysis.

Indexed as

Breast density estimationDense tissuesImage segmentationMammogram assessment

Identifiers

PMID39957743

DOI10.1016/j.dib.2024.110980

PMCPMC11827099

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.