具有基于面积的乳腺密度值、乳腺面积和致密组织分割掩码的乳腺 X 射线图像数据集

IF 1 Q3 MULTIDISCIPLINARY SCIENCES Data in Brief Pub Date : 2024-09-30 DOI:10.1016/j.dib.2024.110980
Hamid Behravan , Naga Raju Gudhe , Hidemi Okuma , Mazen Sudah , Arto Mannermaa
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引用次数: 0

摘要

本文介绍了一个新的数据集,以推动乳房密度自动估算方面的研究,这是乳房X光照片判读的一个关键因素。乳房 X 射线照相术是一种用于乳腺癌筛查的低剂量 X 射线技术,会受到乳房密度的影响。致密组织在乳房 X 光照片上显示为白色,可能会遮挡肿瘤。该数据集以公开的 VinDr-Mammo 数据集为基础,提供了 745 幅乳房 X 光图像(包括训练集和测试集),以及专家-放射线学家对整个乳房和致密组织区域的注释。研究人员可以利用该数据集实现多种目的:训练用于自动乳腺密度分析的深度学习模型,改进用于准确划分乳腺组织的分割方法,以及对现有和新型乳腺密度估计算法进行基准测试。该资源有望通过自动乳腺密度分析的进步改善乳腺癌筛查。
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A dataset of mammography images with area-based breast density values, breast area, and dense tissue segmentation masks
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.
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来源期刊
Data in Brief
Data in Brief MULTIDISCIPLINARY SCIENCES-
CiteScore
3.10
自引率
0.00%
发文量
996
审稿时长
70 days
期刊介绍: Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.
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