Capturing Complexity of the Foot Arch Bones: Evaluation of a Statistical Modelling Framework for Learning Shape, Pose and Intensity Features in a Continuous Domain

Namayega C, Borotikar B, Menten M, Gibbon V, Thusini X, Egger B, Burssens A, Audenaert E, Mutsvangwa TE (2025)

Publication Language: English

Publication Type: Conference contribution

Publication year: 2025

Journal

Communications in Computer and Information Science Springer Verlag

Publisher: Springer

Series: Communications in Computer and Information Science

City/Town: Cham

Book Volume: 2240

Pages Range: 153-163

Conference Proceedings Title: Medical Information Computing. First MICCAI Meets Africa Workshop, MImA 2024, and First MICCAI Student Board Workshop on Empowering Medical Information Computing and Research through Early-Career Expertise, EMERGE 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Revised Selected Papers

Event location: Marrakesh MA

ISBN: 9783031791024

DOI: 10.1007/978-3-031-79103-1_16

Abstract

Advances in medical imaging have enabled detailed digitisation and representation of human anatomy, but challenges remain when modelling complex structures. Statistical models have been developed to capture variations in shape, pose, and intensity features of anatomical structures. However, these models often embed a single feature. This paper investigates how a novel dynamic multi-feature-class Gaussian process modelling framework (DMFC-GPM) designed to learn shape, pose, and intensity features in continuous domains, facilitates the modelling of complex anatomical structures. The work evaluates the framework’s ability to capture multi-feature variations within complex anatomy. Computed tomography image data was processed to build and validate a statistical shape, pose and intensity neutral-arched foot model (12 bones). Framework evaluation was done by validation of the model using specificity, and generality. Fitting the model globally to all objects resulted in specificity and generality reported as average root mean square (RMS) of 0.61 ± 0.11 mm and 1.02 ± 0.21 mm, and average Hausdorff distance (Hd) of 3.47 ± 0.98 mm and 7.56 ± 1.33 mm, respectively. Further validation of the model marginalised to the talus bone resulted in specificity and generality of 0.81 ± 0.25 mm and 1.20 ± 0.48 mm average RMS and 3.24 ± 1.10 mm and 6.50 ± 2.34 mm average Hd, respectively. The talus model variations were consistent with literature. Thus, the novel DMFC-GPM framework can model complex anatomies such as the foot arch.

Authors with CRIS profile

Bernhard Egger Juniorprofessur für Cognitive Computer Vision (Stiftungsprofessur)

Involved external institutions

University of Cape Town (UCT) ZA South Africa (ZA) Technische Universität München (TUM) DE Germany (DE) University Hospital Ghent BE Belgium (BE)

How to cite

APA:

Namayega, C., Borotikar, B., Menten, M., Gibbon, V., Thusini, X., Egger, B.,... Mutsvangwa, T.E. (2025). Capturing Complexity of the Foot Arch Bones: Evaluation of a Statistical Modelling Framework for Learning Shape, Pose and Intensity Features in a Continuous Domain. In Udunna Anazodo, Naren Akash, Moritz Fuchs, Celia Cintas, Alessandro Crimi, Tinahse Mutsvangwa, Farouk Dako, Willam Ogallo (Eds.), Medical Information Computing. First MICCAI Meets Africa Workshop, MImA 2024, and First MICCAI Student Board Workshop on Empowering Medical Information Computing and Research through Early-Career Expertise, EMERGE 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Revised Selected Papers (pp. 153-163). Marrakesh, MA: Cham: Springer.

MLA:

Namayega, Catherine, et al. "Capturing Complexity of the Foot Arch Bones: Evaluation of a Statistical Modelling Framework for Learning Shape, Pose and Intensity Features in a Continuous Domain." Proceedings of the 1st MICCAI Meets Africa Workshop, MImA 2024 and 1st MICCAI Student Board Workshop on Empowering Medical Information Computing and Research through Early-Career Expertise, EMERGE 2024, Held in Conjunction with MICCAI 2024, Marrakesh Ed. Udunna Anazodo, Naren Akash, Moritz Fuchs, Celia Cintas, Alessandro Crimi, Tinahse Mutsvangwa, Farouk Dako, Willam Ogallo, Cham: Springer, 2025. 153-163.

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