What is it about?
Muscles enable all elected movement of the human body. Muscle characteristics such as volume, length, and level of fatty infiltration give insight into the functional capacity of a persons muscles. Muscle segmentation allows these characteristics to be gathered from medical images. An automatic muscle segmentation pipeline was designed and tested to automatically characterise individual muscles from magnetic resonance images, which could be used to facilitate large scale investigations into muscle structure and its response to musculoskeletal disorders.
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Photo by National Cancer Institute on Unsplash
Why is it important?
Automatic tissue segmentation allows quantitatively informed investigations into the state of health of the tissues with the body. Unlike many other tissues within the body, a rigorous method to automatically segment individual muscles from medical images has not yet been proposed. This paper seeks to amend this shortcoming noted within the current literature.
Perspectives
This article was the culmination of 2 years of learning and research about the important and dynamic topic of computational medicine. I hope that this paper promotes the idea that medicine is still a changing science, and innovative computational methods can push the field forward.
William Henson
University of Sheffield
Read the Original
This page is a summary of: Deformable image registration based on single or multi-atlas methods for automatic muscle segmentation and the generation of augmented imaging datasets, PLoS ONE, March 2023, PLOS,
DOI: 10.1371/journal.pone.0273446.
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