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Shape variation analyzer: a classifier for temporomandibular joint damaged by osteoarthritis
Author(s): Nina Tubau Ribera; Priscille de Dumast; Marilia Yatabe; Antonio Ruellas; Marcos Ioshida; Beatriz Paniagua; Martin Styner; João Roberto Gonçalves; Jonas Bianchi; Lucia Cevidanes; Juan-Carlos Prieto
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Paper Abstract

We developed a deep learning neural network, the Shape Variation Analyzer (SVA), that allows disease staging of bony changes in temporomandibular joint (TMJ) osteoarthritis (OA). The sample was composed of 259 TMJ CBCT scans for the training set and 34 for the testing dataset. The 3D meshes had been previously classified in 6 groups by 2 expert clinicians. We improved the robustness of the training data using data augmentation, SMOTE, to alleviate over-fitting and to balance classes. We combined geometrical features and a shape descriptor, heat kernel signature, to describe every shape. The results were compared to nine different supervised machine learning algorithms. The deep learning neural network was the most accurate for classification of TMJ OA. In conclusion, SVA is a 3D Slicer extension that classifies pathology of the temporomandibular joint osteoarthritis cases based on 3D morphology.

Paper Details

Date Published: 13 March 2019
PDF: 7 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 1095021 (13 March 2019); doi: 10.1117/12.2506018
Show Author Affiliations
Nina Tubau Ribera, Univ. of Michigan (United States)
Priscille de Dumast, Univ. of Michigan (United States)
Marilia Yatabe, Univ. of Michigan (United States)
Antonio Ruellas, Univ. of Michigan (United States)
Marcos Ioshida, Univ. of Michigan (United States)
Beatriz Paniagua, Kitware, Inc. (United States)
Martin Styner, The Univ. of North Carolina at Chapel Hill (United States)
João Roberto Gonçalves, São Paulo State Univ. School of Dentistry (Brazil)
Jonas Bianchi, Univ. of Michigan (United States)
São Paulo State Univ. School of Dentistry (Brazil)
Lucia Cevidanes, Univ. of Michigan (United States)
Juan-Carlos Prieto, The Univ. of North Carolina at Chapel Hill (United States)

Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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