Detection of abnormal extraocular muscles in small datasets of computed tomography images using a three-dimensional variational autoencoder.

ABSTRACT: We sought to establish an unsupervised algorithm with a three-dimensional (3D) variational autoencoder model (VAE) for the detection of abnormal extraocular muscles in small datasets of orbital computed tomography (CT) images. 334 CT images of normal orbits and 96 of abnormal orbits diagnosed as thyroid eye disease were used for training and validation; 24 normal and 11 abnormal orbits were used for the test. A 3D VAE was developed and trained. All images were preprocessed to emphasize extraocular muscles and to suppress background noise (e.g., high signal intensity from bones). The optimal cut-off value was identified through receiver operating characteristic (ROC) curve analysis. The ability of the model to detect muscles of abnormal size was assessed by visualization. The model achieved

SUBMITTER: Chung YW 

PROVIDER: S-EPMC9889739 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature