An annotated heterogeneous ultrasound database

Scientific Data, 2025
Yuezhe Yang1*, Yonglin Chen2*, Xingbo Dong1†, Junning Zhang3, Chihui Long4, Zhe Jin1, Yong Dai3
*Equal Contribution †Corresponding author
1Anhui University, 2Anhui Jianzhu University, 3Anhui University of Science and Technology, 4Tongren Hospital of Wuhan University
Paper Data Code

Abstract

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, diagnosing abnormalities in ultrasound images is particularly challenging for less experienced clinicians.

The development of artificial intelligence (AI) 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 models. Therefore, we have collected a large, publicly accessible ultrasound challenge database that is intended to significantly enhance the performance of AI-assisted ultrasound diagnosis. This database 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 disease diagnosis in multi-center environments.

Highlights

  • First to use public media to obtain valuable medical video data.
  • Build a large and annotated ultrasound video diagnostic dataset.
  • Introduce a new benchmark for ultrasound video classification.

Method

Method illustration

Flowcharts of the annotation process and data preprocessing.

Data Demo

Citation

@article{yang2025annotated,
  title={An annotated heterogeneous ultrasound database},
  author={Yang, Yuezhe and Chen, Yonglin and Dong, Xingbo and Zhang, Junning and Long, Chihui and Jin, Zhe and Dai, Yong},
  journal={Scientific Data},
  volume={12},
  number={1},
  pages={148},
  year={2025},
  publisher={Nature Publishing Group UK London}
}