Learning osteoarthritis imaging biomarkers from bone surface spherical encoding.

ABSTRACT:

Purpose

To learn bone shape features from spherical bone map of knee MRI images using established convolutional neural networks (CNN) and use these features to diagnose and predict osteoarthritis (OA).

Methods

A bone segmentation model was trained on 25 manually annotated 3D MRI volumes to segment the femur, tibia, and patella from 47 078 3D MRI volumes. Each bone segmentation was converted to a 3D point cloud and transformed into spherical coordinates. Different fusion strategies were performed to merge spherical maps obtained by each bone. A total of 41 822 merged spherical maps with corresponding Kellgren-Lawrence grades for radiographic OA were used to train a CNN classifier model to diagnose OA using bone shape learned features. Several OA Diagnosis models were tested a

SUBMITTER: Morales Martinez A 

PROVIDER: S-EPMC7329596 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature