BUS-UCLM: Breast ultrasound lesion segmentation dataset

This dataset comprises 38 breast ultrasound scans from patients, encompassing a total of 683 images. The scans were conducted using a Siemens ACUSON S2000TM Ultrasound System from 2022 to 2023. The dataset is specifically created for the purpose of segmenting breast lesions, with the goal of identifying the area and contour of the lesion, as well as classifying it as either benign or malignant. The images can be classified into three categories based on their findings: 419 are normal, 174 are benign, and 90 are malignant. The ground truth is given as RGB segmentation masks in individual files, with black indicating normal breast tissue and green and red indicating benign and malignant lesions, respectively. This dataset enables researchers to construct and evaluate machine learning models for identifying between benign and malignant tumours in authentic breast ultrasound images. The segmentation annotations provided by expert radiologists enable accurate model training and evaluation, making this dataset a valuable asset in the field of computer vision and public health.