Deep-Learning Based Automated Segmentation and Quantitative Volumetric Analysis of Orbital Muscle and Fat for Diagnosis of Thyroid Eye Disease

• Published in: Investigative ophthalmology & visual science Vol. 65; no. 5; p. 6
• Main Authors: Alkhadrawi, Adham M., Lin, Lisa Y., Langarica, Saul A., Kim, Kyungsu, Ha, Sierra K., Lee, Nahyoung G., Do, Synho
• Format: Journal Article
• Language: English
• Published: United States The Association for Research in Vision and Ophthalmology 01.05.2024
• Subjects: Adipose Tissue - diagnostic imaging; Adult; Aged; Deep Learning; Female; Graves Ophthalmopathy - diagnosis; Graves Ophthalmopathy - diagnostic imaging; Humans; Male; Middle Aged; Multidisciplinary Ophthalmic Imaging; Oculomotor Muscles - diagnostic imaging; Orbit - diagnostic imaging; Organ Size; Retrospective Studies; ROC Curve; Tomography, X-Ray Computed - methods
• ISSN: 1552-5783; 0146-0404; 1552-5783
• DOI: 10.1167/iovs.65.5.6

Thyroid eye disease (TED) is characterized by proliferation of orbital tissues and complicated by compressive optic neuropathy (CON). This study aims to utilize a deep-learning (DL)-based automated segmentation model to segment orbital muscle and fat volumes on computed tomography (CT) images and provide quantitative volumetric data and a machine learning (ML)-based classifier to distinguish between TED and TED with CON. Subjects with TED who underwent clinical evaluation and orbital CT imaging were included. Patients with clinical features of CON were classified as having severe TED, and those without were classified as having mild TED. Normal subjects were used for controls. A U-Net DL-model was used for automatic segmentation of orbital muscle and fat volumes from orbital CTs, and ensemble of Random Forest Classifiers were used for volumetric analysis of muscle and fat. Two hundred eighty-one subjects were included in this study. Automatic segmentation of orbital tissues was performed. Dice coefficient was recorded to be 0.902 and 0.921 for muscle and fat volumes, respectively. Muscle volumes among normal, mild, and severe TED were found to be statistically different. A classification model utilizing volume data and limited patient data had an accuracy of 0.838 and an area under the curve (AUC) of 0.929 in predicting normal, mild TED, and severe TED. DL-based automated segmentation of orbital images for patients with TED was found to be accurate and efficient. An ML-based classification model using volumetrics and metadata led to high diagnostic accuracy in distinguishing TED and TED with CON. By enabling rapid and precise volumetric assessment, this may be a useful tool in future clinical studies.