BUS-UCLM: Breast ultrasound lesion segmentation dataset

The proposed dataset is comprised of breast ultrasound images from 38 patients. It consists of 683 images, of which 174 are benign, 90 are malignant, and 419 are normal. Scans were obtained with a Siemens ACUSON S2000TM Ultrasound System between 2022 and 2023. The ground truth is presented in separate files as RGB segmentation masks where green denotes benign lesions, red denotes malignant lesions, and black denotes the background or normal breast tissue. This dataset constitutes a valuable resource for research in breast cancer diagnosis, lesion detection, medical imaging, and health care applications. It facilitates researchers and practitioners to develop and examine machine learning models for the identification of benign and malignant lesions across full real cases. The segmentation annotations made by expert radiologists enable precise model training and evaluation, making this dataset a benefit in the field of computer vision and public health.

本研究所提出的数据集由来自38名患者的乳腺超声图像构成。该数据集包含683张图像，其中良性图像174张，恶性图像90张，正常图像419张。图像采集于2022年至2023年使用西门子ACUSON S2000TM超声系统完成。真实标签以独立文件的形式呈现，以RGB分割掩码的形式展现，其中绿色代表良性病变，红色代表恶性病变，黑色代表背景或正常乳腺组织。此数据集在乳腺癌诊断、病灶检测、医学成像及健康管理应用研究中具有重要价值。它有助于研究人员和从业者开发和检验针对完整真实案例中良性及恶性病灶识别的机器学习模型。由资深放射科医师制作的分割标注为模型的精确训练与评估提供了便利，使得该数据集在计算机视觉和公共卫生领域具有显著优势。

搜集汇总

背景与挑战

背景概述

该数据集包含38名患者的乳腺超声图像，共计683张，其中174张良性、90张恶性和419张正常图像。数据集提供了由专业放射科医生标注的RGB分割掩码，用于乳腺癌诊断、病灶检测和医学影像研究，适用于计算机视觉和公共健康领域的模型开发和评估。