BUS-UCLM: Breast ultrasound lesion segmentation dataset

Published: 26 February 2024| Version 1 | DOI: 10.17632/7fvgj4jsp7.1
Noelia Vallez,


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.



Universidad de Castilla-La Mancha, Hospital General de Ciudad Real


Breast Cancer, Image Segmentation, Object Detection, Ultrasound, Breast Ultrasonography, Instance Segmentation


Ministerio de Ciencia, Innovación y Universidades


European Union NextGenerationEU/PRTR