Deformable image registration based on single or multi-atlas methods for automatic muscle segmentation and the generation of augmented imaging datasets

• Published in: PloS one Vol. 18; no. 3; p. e0273446
• Main Authors: Henson, William H., Mazzá, Claudia, Dall’Ara, Enrico
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 10.03.2023; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Biology and Life Sciences; Computer and Information Sciences; CT imaging; Datasets; Deep learning; Deformation; Formability; Geometry; Humans; Image processing; Image Processing, Computer-Assisted - methods; Image registration; Image segmentation; Limbs; Longitudinal studies; Magnetic resonance; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical imaging; Medicine and Health Sciences; Methods; Muscles; Physical work; Physiological aspects; Probabilistic methods; Registration; Research and Analysis Methods; Segmentation; Skeletal muscle; United Kingdom
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0273446

Muscle segmentation is a process relied upon to gather medical image-based muscle characterisation, useful in directly assessing muscle volume and geometry, that can be used as inputs to musculoskeletal modelling pipelines. Manual or semi-automatic techniques are typically employed to segment the muscles and quantify their properties, but they require significant manual labour and incur operator repeatability issues. In this study an automatic process is presented, aiming to segment all lower limb muscles from Magnetic Resonance (MR) imaging data simultaneously using three-dimensional (3D) deformable image registration (single inputs or multi-atlas). Twenty-three of the major lower limb skeletal muscles were segmented from five subjects, with an average Dice similarity coefficient of 0.72, and average absolute relative volume error (RVE) of 12.7% (average relative volume error of -2.2%) considering the optimal subject combinations. The multi-atlas approach showed slightly better accuracy (average DSC: 0.73; average RVE: 1.67%). Segmented MR imaging datasets of the lower limb are not widely available in the literature, limiting the potential of new, probabilistic methods such as deep learning to be used in the context of muscle segmentation. In this work, Non-linear deformable image registration is used to generate 69 manually checked, segmented, 3D, artificial datasets, allowing access for future studies to use these new methods, with a large amount of reliable reference data.