Machine learning to extract muscle fascicle length changes from dynamic ultrasound images in real-time
Abstract
Dynamic muscle fascicle length measurements through B-mode ultrasound have become popular for the non-invasive physiological insights they provide regarding musculoskeletal structure-function. However, current practices typically require time consuming post-processing to track muscle length changes from B-mode images. A real-time measurement tool would not only save processing time but would also help pave the way toward closed-loop applications based on feedback signals driven by in vivo muscle length change patterns. In this paper, we benchmark an approach that combines traditional machine learning (ML) models with B-mode ultrasound recordings to obtain muscle fascicle length changes in real-time. To gauge the utility of this framework for ‘in-the-loop’ applications, we evaluate accuracy of the extracted muscle length change signals against time-series’ derived from a standard, post-hoc automated tracking algorithm.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- May 26, 2021
- Source ID
- 10.1371/journal.pone.0246611
Entities
People
- Gregory S. Sawicki
- Jonathan S. Zia
- Luis G. Rosa
- Omer T. Inan
Organizations
- National Institute of Biomedical Imaging and Bioengineering
- National Institute on Aging
- National Science Foundation
- United States Army Natick Soldier Research, Development and Engineering Center