Deep Learning Methods for Muscle Analysis From Magnetic Resonance Images
type: CreativeWork
description: Quantitative analyses of skeletal muscles inform diverse fields, from assessing training impacts in sports science to muscular diseases in medicine, to name a few. Medical imaging offers noninvasive tools for muscle size and structure analysis, typically necessitating pixel/voxel-wise annotations. Segmentation frameworks utilising deep learning offer a data-driven approach to automate the annotation process efficiently and accurately. However, their performance is typically dependent on the nature, distribution and size of the training data. Four studies are presented in this thesis to enable automatic segmentation of skeletal muscles in various scenarios. The first study systematically evaluated the efficacy of 2D, 3D, and hybrid models in segmenting skeletal muscles from anisotropic magnetic resonance imaging (MRI) scans and concluded that the hybrid model demonstrated the best performance. This finding is used in the second study, where a novel hybrid model is proposed. It also demonstrated human-level performance on isotropic MRI scans, showing the potential to automate the annotation process for a wide range of data. The third study focuses on medical image segmentation in the semi-supervised setting and proposes a hybrid model based on the mean-teacher network. The proposed hybrid model, which we named HD-Teacher, conducts segmentation with hybrid features extracted from 2D and 3D dimensions. In addition, it requires minimal labelled training data and can utilise unlabelled data to improve its performance through a novel hybrid regularisation mechanism. Its practicality is further demonstrated through its state-of-the-art performance on musculoskeletal data. We also further evaluate its performance on two other datasets with varying anisotropy to showcase its generalisability. The fourth study focuses on domain adaptation to overcome the challenge that most deep learning models trained on one specific type of image (source domain) typically do not perform well…
abstract: Quantitative analyses of skeletal muscles inform diverse fields, from assessing training impacts in sports science to muscular diseases in medicine, to name a few. Medical imaging offers noninvasive tools for muscle size and structure analysis, typically necessitating pixel/voxel-wise annotations. Segmentation frameworks utilising deep learning offer a data-driven approach to automate the annotation process efficiently and accurately. However, their performance is typically dependent on the nature, distribution and size of the training data. Four studies are presented in this thesis to enable automatic segmentation of skeletal muscles in various scenarios. The first study systematically evaluated the efficacy of 2D, 3D, and hybrid models in segmenting skeletal muscles from anisotropic magnetic resonance imaging (MRI) scans and concluded that the hybrid model demonstrated the best performance. This finding is used in the second study, where a novel hybrid model is proposed. It also demonstrated human-level performance on isotropic MRI scans, showing the potential to automate the annotation process for a wide range of data. The third study focuses on medical image segmentation in the semi-supervised setting and proposes a hybrid model based on the mean-teacher network. The proposed hybrid model, which we named HD-Teacher, conducts segmentation with hybrid features extracted from 2D and 3D dimensions. In addition, it requires minimal labelled training data and can utilise unlabelled data to improve its performance through a novel hybrid regularisation mechanism. Its practicality is further demonstrated through its state-of-the-art performance on musculoskeletal data. We also further evaluate its performance on two other datasets with varying anisotropy to showcase its generalisability. The fourth study focuses on domain adaptation to overcome the challenge that most deep learning models trained on one specific type of image (source domain) typically do not perform well…
author: Zhu, Jiayi ; https://orcid.org/0000-0003-3837-7631
language: en
keywords: Computer Vision, Deep Learning, Medical Image Analysis, MRI, CT, anzsrc-for: 461103 Deep learning, anzsrc-for: 4603 Computer vision and multimedia computation, anzsrc-for: 460306 Image processing, anzsrc-for: 4611 Machine learning
license: https://purl.org/coar/access_right/c_abf2
license: https://creativecommons.org/licenses/by/4.0/
date published: 2024
date modified: 2026-07-24T05:33:55Z
provider: University of New South Wales
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: UNSW, Sydney

PropertyValue
property ID: Handle
value: http://hdl.handle.net/1959.4/102882

PropertyValue
property ID: DOI
value: 10.26190/unsworks/30447