UMUD: A Web Application for Easy Access to Musculoskeletal Ultrasonography Datasets

Abstract

Introduction & Purpose: Ultrasonography is widely used to assess skeletal muscle and tendon properties, such as architecture, cross-sectional area, and tissue stiffness (Sarto et al., 2021). Despite its growing application in different scenarios, and the increasing call for open data access and sharing in clinical research, there remains a significant scarcity of public datasets in this field. This lack of accessible and standardized public datasets limits large-scale analysis algorithm studies, trainee training and the development of image analysis algorithms. To address this, we developed the Universal Musculoskeletal Ultrasonography Database (UMUD), a web application designed to facilitate access to these datasets and foster standardization and innovation in musculoskeletal ultrasonography imaging research.

Methods: UMUD is an online repository that aggregates and indexes metadata from publicly available musculoskeletal ultrasonography datasets hosted on platforms like the Open Science Framework and Zenodo. The web application (https://universalmuscledatabase.streamlit.app/) is built using a streamlit (v1.35.0) frontend and mongoDB for its database infrastructure. Standardized metadata descriptors, i.e., muscle group, ultrasound device, participant demographics, are implemented using a combination of pydantic models (v1.10.0) and json schemata to ensure reproducibility and ease of usage for contributing data. UMUD provides detailed instructions for community contributions, including tools for data anonymization.

Results: Currently, UMUD hosts 11 datasets from 10 studies, comprising 75,569 images and 2,573 videos from 1,769 participants. The database covers nine lower-limb muscles and one muscle–tendon junction (distal triceps surae and Achilles tendon), captured using various modalities including static imaging, dynamic video, and 3D reconstructions. Datasets include measurements across proximal, middle, and distal regions of each muscle. Benchmark datasets are provided for trainee training and algorithm evaluation, including multi-expert annotated images, fascicle and cross-sectional area overlays, and fully labeled datasets for deep-learning model training. Additionally, UMUD lists available automated image analysis algorithms as a reference for community use.

Discussion: UMUD provides an initial foundation for open, standardized, and community-driven musculoskeletal ultrasound research. By centralizing datasets and metadata, it facilitates reproducible research, algorithm benchmarking, and operator education. The inclusion of multi-expert and labeled benchmark datasets supports both training and the development of automated analysis methods. Future directions include expanding dataset coverage, enhancing interactive visualization tools, and launching community challenges for algorithm benchmarking to accelerate innovation in the field.

Conclusions: In conclusion, UMUD addresses relevant challenges in musculoskeletal ultrasonography by providing a centralized, standardized repository of datasets and tools. It promotes transparency and innovation in the field, supporting reproducible research and advancements in automated image analysis. Future developments include adding datasets, expanding functionalities and introducing community-driven algorithm development challenges.

References

Sarto, F., Spörri, J., Fitze, D. P., Quinlan, J. I., Narici, M. V., & Franchi, M. V. (2021). Implementing Ultrasound Imaging for the Assessment of Muscle and Tendon Properties in Elite Sports: Practical Aspects, Methodological Considerations and Future Directions. Sports Med, 51(6), 1151–1170. https://doi.org/10.1007/s40279-021-01436-7