Geometric morphometrics for 3D dense surface correspondence: population comparisons of shoulder bone morphology

Background: Comparisons in morphological shape/form across population groups could provide population differences that might assist in making decisions on diagnosis and prognosis by the clinician. Geometric morphometrics (GM) is one of the fields that help to provide such population comparisons. In medical imaging and related disciplines, GM is commonly done using annotated landmarks or distances measured from 3D surfaces (consisting of triangular meshes). However, these landmarks may not be sufficient to describe the complete shape. This project aimed to develop GM for analysis that consider all vertices in the triangular mesh as landmarks. The developed methods were applied to South African and Swiss shoulder bones (scapula and humerus) to analyse morphological differences. Methods: The developed pipeline required first establishing correspondence across the datasets through a registration process. Gaussian process fitting was chosen to perform the registration since it is considered state-of-the-art. Secondly, a novel method for automatic identification of vertices or areas encoding the most shape/form variation was developed. Thirdly, a principal component analysis (PCA) that addressed the high dimensionality and lower sample size (HDLSS) phenomenon was adopted and applied to the dense correspondence data. This approach allowed for the stabilisation of the distribution of the data in low-dimensional form/shape space. Lastly, appropriate statistical tests were developed for population comparisons of the shoulder bones when dealing with HDLSS data in both form and shape space. Results: When the mesh-based GM analysis approach was applied to the training datasets (South African and Swiss shoulder bones), it was found that the anterior glenoid which is often the site of the shoulder dislocation is the most varied area of the glenoid. This has implications for diagnosis and provides knowledge for prosthesis design. The distribution of the data in the modified PCA space was shown to converge to a stable distribution when more vertices/landmarks are used for the analysis. South African and Swiss datasets were shown to be more distinguishable in a low-dimensional space when considering form rather than shape. It was found that left and right South African scapula bones are significantly different in terms of shape. Discussion: In general, it was observed that the two populations means can be significantly different in shape but not in form. An improved understanding of these observed shape and form differences has utility for shoulder arthroplasty prosthesis design and may also be useful for orthopaedic surgeons during surgical preoperative planning.

Grantor

Department of Human Biology

Year dc:date.issued

2018

Author dc:creator

• Fouefack, Jean-Rassaire

Advisors dc:contributor.advisor

• Mutsvangwa, Tinashe
• Douglas, Tania
• Inyang, Adijat Omowumi

• Biomedical Engineering

Handle dc:identifier.uri

http://hdl.handle.net/11427/30024

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/30024