Mammogram Density Assessment Dataset
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GENERAL OVERVIEW This dataset was compiled to address the limitations of current methods for breast density assessment in mammograms, especially the challenges of: • Shortage of radiologists: There are not enough radiologists to efficiently analyze the large number of mammograms needed for screening. • Subjectivity: Radiologist assessments of breast density can vary, leading to inconsistencies. • Limitations of existing tools: Current CAD tools for breast density estimation often have limitations, such as restricted functionality to specific mammogram views and difficulties with accurate segmentation. This dataset offers a unique solution by expanding the original mammogram images with: • Binary masks of the breast area: These expert-annotated masks precisely delineate the entire breast region in each mammogram, providing valuable ground truth data for segmentation methods. • Binary masks of dense tissue: Similarly, these masks accurately identify areas of dense tissue within each mammogram, further enhancing the dataset's utility for training and evaluating segmentation algorithms. The dataset facilitates the development of automated breast density estimation with deep learning. It also serves as a valuable tool for researchers developing and benchmarking medical image segmentation methods specifically focused on breast tissue analysis in mammograms. DATA DESCRIPTION This dataset consists of segmentation masks for dense tissue and breast area as well as area-based breast density percentage values from the VinDr-Mammo public dataset accessible from [3]. All annotations were performed and validated by an expert radiologist. Files: The data is provided in two compressed archives, ‘train.zip’ and ‘test.zip’. • train.zip: This archive contains two sub-folders: - breast_masks: This sub-folder contains the ground truth segmentation masks for the breast area, also in JPG format. - dense_masks: This sub-folder contains the ground truth segmentation masks for the dense tissue, again in JPG format. The segmentation masks have the dimensions of 2800×3518 pixels. File Lists: Two CSV files are provided alongside the compressed archives: • train.csv: This file contains information about the training set with two columns: - Filename: This column contains the filenames of the training set images. These images can be directly downloaded from the VinDr-Mammo dataset, https://physionet.org/content/vindr-mammo/1.0.0/. - Density: This column provides the ground truth continuous breast density value for each mammogram in the training set, intended for the breast density estimation task. • test.csv: This file contains a single column, “Filename”, listing the filenames of the test set. No ground truth information is provided for the test set. Ground truths are intentionally kept private for Breast Density Kaggle Challenge https://www.kaggle.com/competitions/breast-density-prediction, however, will be eventually open to public in the dataset repository.
总体概述 本数据集旨在解决当前乳腺X线摄影乳腺密度评估方法的局限性，尤其针对以下几类挑战： • 放射医师短缺：当前乳腺筛查所需的乳腺X线影像总量庞大，现有放射医师人力不足以高效完成全部分析工作。 • 评估主观性问题：不同放射医师对乳腺密度的评估结果存在显著差异，易引发评估结果不一致的问题。 • 现有工具存在局限：当前用于乳腺密度估计的计算机辅助诊断（CAD，Computer-Aided Diagnosis）工具往往存在诸多限制，例如仅支持特定乳腺X线摄影视图的功能受限，以及精准分割存在难度。 本数据集通过为原始乳腺X线影像补充以下两类标注数据，提供了独特的解决方案： • 乳腺区域二值掩码：此类经专家标注的掩码可精准勾勒每张乳腺X线影像中的完整乳腺区域，为分割算法研发提供了极具价值的真实标注（ground truth）数据。 • 致密组织二值掩码：此类掩码可精准识别每张影像内的致密组织区域，进一步提升了本数据集在训练与评估分割算法方面的实用价值。 本数据集可推动基于深度学习的自动化乳腺密度估计技术的研发，同时也可为致力于开发并基准测试乳腺X线影像乳腺组织分析相关医学图像分割方法的研究人员提供宝贵的研究工具。 数据详情 本数据集包含致密组织与乳腺区域的分割掩码，以及源自可通过[3]获取的VinDr-Mammo公开数据集的基于区域计算的乳腺密度百分比数值。所有标注工作均由专业放射医师完成并经其审核验证。 文件说明 数据集以两个压缩归档文件"train.zip"与"test.zip"形式提供。 • train.zip：该归档包含两个子文件夹： - breast_masks：此子文件夹存储乳腺区域分割掩码的真实标注数据，文件格式为JPG。 - dense_masks：此子文件夹存储致密组织分割掩码的真实标注数据，文件格式同样为JPG。 所有分割掩码的像素尺寸均为2800×3518。 文件列表 除压缩归档外，本数据集还附带两个CSV格式文件： • train.csv：该文件包含训练集相关信息，共包含两列： - Filename（文件名）：该列存储训练集影像的文件名，相关影像可直接从VinDr-Mammo数据集下载，下载地址为https://physionet.org/content/vindr-mammo/1.0.0/。 - Density（密度值）：该列提供训练集每张乳腺X线影像对应的连续型乳腺密度真实标注值，适用于乳腺密度估计任务。 • test.csv：该文件仅包含一列"Filename"，列出测试集影像的文件名。测试集未提供任何真实标注信息。 为配合乳腺密度预测Kaggle竞赛（https://www.kaggle.com/competitions/breast-density-prediction）的赛事要求，测试集的真实标注信息暂未公开，但最终将在数据集仓库中向公众开放。
