An annotated heterogeneous ultrasound database

Ultrasound is a primary diagnostic tool commonly used to evaluate internal body structures, including organs, blood vessels, the musculoskeletal system, and fetal development. Due to challenges such as operator dependence, noise, limited field of view, difficulty in imaging through bone and air, and variability across different systems make diagnosing abnormalities in ultrasound images particularly challenging for less experienced clinicians. The development of artificial intelligence technology could assist in the diagnosis of ultrasound images. However, many databases are created using a single device type and collection site, limiting the generalizability of machine learning classification models. Therefore, we have collected a large, publicly accessible ultrasound challenge database that is intended to significantly enhance the performance of traditional ultrasound image classification. This dataset is derived from publicly available data on the Internet and comprises a total of 1,833 distinct ultrasound data. It includes 13 different ultrasound image anomalies, and all data have been anonymized. Our data-sharing program aims to support benchmark testing of ultrasound image disease diagnosis and classification accuracy in multicenter environments.

超声是临床常用的一线诊断工具，常用于评估人体内部组织结构，涵盖脏器、血管、肌肉骨骼系统以及胎儿发育情况。受操作者依赖、图像噪声、视野受限、骨与空气遮挡下成像困难，以及不同设备系统间存在差异等诸多挑战影响，经验不足的临床医师在超声图像中诊断异常病变时往往颇具难度。人工智能技术的发展可为超声图像诊断提供辅助支持。然而，现有多数数据库仅采用单一设备类型与单一采集站点构建，这限制了机器学习分类模型的泛化能力。为此，本研究采集了一个大规模、可公开获取的超声挑战数据集，旨在显著提升传统超声图像分类模型的性能表现。本数据集源自互联网公开数据，共计包含1833组独立超声数据，涵盖13类超声图像异常病变，且所有数据均已完成匿名化处理。本数据共享计划旨在支持多中心环境下的超声图像疾病诊断与分类准确率基准测试。