Articulated Statistical Shape Modelling of the Shoulder Joint

 

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dc.contributor.advisor Mutsvangwa, Tinashe
dc.contributor.advisor Douglas, Tania
dc.contributor.author Alemneh, Tewodros
dc.date.accessioned 2020-09-09T14:57:25Z
dc.date.available 2020-09-09T14:57:25Z
dc.date.issued 2020_
dc.identifier.citation Alemneh, T. 2020. Articulated Statistical Shape Modelling of the Shoulder Joint. . ,Faculty of Health Sciences ,Department of Human Biology. http://hdl.handle.net/11427/32190 en_ZA
dc.identifier.uri http://hdl.handle.net/11427/32190
dc.description.abstract The shoulder joint is the most mobile and unstable joint in the human body. This makes it vulnerable to soft tissue pathologies and dislocation. Insight into the kinematics of the joint may enable improved diagnosis and treatment of different shoulder pathologies. Shoulder joint kinematics can be influenced by the articular geometry of the joint. The aim of this project was to develop an analysis framework for shoulder joint kinematics via the use of articulated statistical shape models (ASSMs). Articulated statistical shape models extend conventional statistical shape models by combining the shape variability of anatomical objects collected from different subjects (statistical shape models), with the physical variation of pose between the same objects (articulation). The developed pipeline involved manual annotation of anatomical landmarks selected on 3D surface meshes of scapulae and humeri and establishing dense surface correspondence across these data through a registration process. The registration was performed using a Gaussian process morphable model fitting approach. In order to register two objects separately, while keeping their shape and kinematics relationship intact, one of the objects (scapula) was fixed leaving the other (humerus) to be mobile. All the pairs of registered humeri and scapulae were brought back to their native imaged position using the inverse of the associated registration transformation. The glenohumeral rotational center and local anatomic coordinate system of the humeri and scapulae were determined using the definitions suggested by the International Society of Biomechanics. Three motions (flexion, abduction, and internal rotation) were generated using Euler angle sequences. The ASSM of the model was built using principal component analysis and validated. The validation results show that the model adequately estimated the shape and pose encoded in the training data. Developing ASSM of the shoulder joint helps to define the statistical shape and pose parameters of the gleno humeral articulating surfaces. An ASSM of the shoulder joint has potential applications in the analysis and investigation of population-wide joint posture variation and kinematics. Such analyses may include determining and quantifying abnormal articulation of the joint based on the range of motion; understanding of detailed glenohumeral joint function and internal joint measurement; and diagnosis of shoulder pathologies. Future work will involve developing a protocol for encoding the shoulder ASSM with real, rather than handcrafted, pose variation.
dc.subject Biomedical Engineering
dc.title Articulated Statistical Shape Modelling of the Shoulder Joint
dc.type Master Thesis
dc.date.updated 2020-09-09T11:02:21Z
dc.language.rfc3066 eng
dc.publisher.faculty Faculty of Health Sciences
dc.publisher.department Department of Human Biology
dc.type.qualificationlevel Masters
dc.type.qualificationlevel MSc
dc.identifier.apacitation Alemneh, T. (2020). <i>Articulated Statistical Shape Modelling of the Shoulder Joint</i>. (). ,Faculty of Health Sciences ,Department of Human Biology. Retrieved from http://hdl.handle.net/11427/32190 en_ZA
dc.identifier.chicagocitation Alemneh, Tewodros. <i>"Articulated Statistical Shape Modelling of the Shoulder Joint."</i> ., ,Faculty of Health Sciences ,Department of Human Biology, 2020. http://hdl.handle.net/11427/32190 en_ZA
dc.identifier.vancouvercitation Alemneh T. Articulated Statistical Shape Modelling of the Shoulder Joint. []. ,Faculty of Health Sciences ,Department of Human Biology, 2020 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/32190 en_ZA
dc.identifier.ris TY - Master Thesis AU - Alemneh, Tewodros AB - The shoulder joint is the most mobile and unstable joint in the human body. This makes it vulnerable to soft tissue pathologies and dislocation. Insight into the kinematics of the joint may enable improved diagnosis and treatment of different shoulder pathologies. Shoulder joint kinematics can be influenced by the articular geometry of the joint. The aim of this project was to develop an analysis framework for shoulder joint kinematics via the use of articulated statistical shape models (ASSMs). Articulated statistical shape models extend conventional statistical shape models by combining the shape variability of anatomical objects collected from different subjects (statistical shape models), with the physical variation of pose between the same objects (articulation). The developed pipeline involved manual annotation of anatomical landmarks selected on 3D surface meshes of scapulae and humeri and establishing dense surface correspondence across these data through a registration process. The registration was performed using a Gaussian process morphable model fitting approach. In order to register two objects separately, while keeping their shape and kinematics relationship intact, one of the objects (scapula) was fixed leaving the other (humerus) to be mobile. All the pairs of registered humeri and scapulae were brought back to their native imaged position using the inverse of the associated registration transformation. The glenohumeral rotational center and local anatomic coordinate system of the humeri and scapulae were determined using the definitions suggested by the International Society of Biomechanics. Three motions (flexion, abduction, and internal rotation) were generated using Euler angle sequences. The ASSM of the model was built using principal component analysis and validated. The validation results show that the model adequately estimated the shape and pose encoded in the training data. Developing ASSM of the shoulder joint helps to define the statistical shape and pose parameters of the gleno humeral articulating surfaces. An ASSM of the shoulder joint has potential applications in the analysis and investigation of population-wide joint posture variation and kinematics. Such analyses may include determining and quantifying abnormal articulation of the joint based on the range of motion; understanding of detailed glenohumeral joint function and internal joint measurement; and diagnosis of shoulder pathologies. Future work will involve developing a protocol for encoding the shoulder ASSM with real, rather than handcrafted, pose variation. DA - 2020_ DB - OpenUCT DP - University of Cape Town KW - Biomedical Engineering LK - https://open.uct.ac.za PY - 2020 T1 - Articulated Statistical Shape Modelling of the Shoulder Joint TI - Articulated Statistical Shape Modelling of the Shoulder Joint UR - http://hdl.handle.net/11427/32190 ER - en_ZA


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