SVA: shape variation analyzer

Priscille de Dumast; Clement Mirabel; Beatriz Paniagua; Marilia Yatabe; Antonio Ruellas; Nina Tubau; Martin Styner; Lucia Cevidanes; Juan C. Prieto

doi:10.1117/12.2295631

12 March 2018 SVA: shape variation analyzer

Priscille de Dumast, Clement Mirabel, Beatriz Paniagua, Marilia Yatabe, Antonio Ruellas, Nina Tubau, Martin Styner, Lucia Cevidanes, Juan C. Prieto

Author Affiliations +

Proceedings Volume 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging; 105782H (2018) https://doi.org/10.1117/12.2295631
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

Temporo-mandibular osteo arthritis (TMJ OA) is characterized by progressive cartilage degradation and subchondral bone remodeling. The causes of this pathology remain unclear. Current research efforts are concentrated in finding new biomarkers that will help us understand disease progression and ultimately improve the treatment of the disease. In this work, we present Shape Variation Analyzer (SVA), the goal is to develop a noninvasive technique to provide information about shape changes in TMJ OA. SVA uses neural networks to classify morphological variations of 3D models of the mandibular condyle. The shape features used for training include normal vectors, curvature and distances to average models of the condyles. The selected features are purely geometric and are shown to favor the classification task into 6 groups generated by consensus between two clinician experts. With this new approach, we were able to accurately classify 3D models of condyles. In this paper, we present the methods used and the results obtained with this new tool.

Citation Download Citation

Priscille de Dumast, Clement Mirabel, Beatriz Paniagua, Marilia Yatabe, Antonio Ruellas, Nina Tubau, Martin Styner, Lucia Cevidanes, and Juan C. Prieto "SVA: shape variation analyzer", Proc. SPIE 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, 105782H (12 March 2018); https://doi.org/10.1117/12.2295631

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