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Semantic Segmentation of Knee Articular Cartilage

posted on 21.01.2021, 02:06 by SOMAYYA EBRAHIMKHANI
The main objective of this thesis is to identify an automatic knee articular cartilage tissue (ACT) segmentation method using existing deep learning-based methods and develop a novel segmentation framework by employing conventional machine learning techniques as a component to improve segmentation accuracy. The following properties should be integrated into such ACT segmentation framework: (1) implementation of a deep learning model to perform automatic segmentation of knee joint, (2) incorporation of the shape information to capture shape variations of the knees within a population of healthy and pathologic subjects, and (3) extraction of more sophisticated ACT-specific feature sets from the corresponding knee ACT regions.


Campus location


Principal supervisor

Anuja Dharmaratne

Additional supervisor 1

Mohamed Hisham Jaward

Additional supervisor 2

Flavia M. Cicuttini

Additional supervisor 3

Yuanyuan Wang

Year of Award


Department, School or Centre

School of Information Technology (Monash University Malaysia)


Doctor of Philosophy

Degree Type