Semantic segmentation using deep learning to extract total extraocular muscles and optic nerve from orbital computed tomography images

Document Type

Article

Publication Date

10-2021

Department

Department of Applied Computing

Abstract

Precise segmentation of total extraocular muscles (EOM) and optic nerve (ON) is essential to assess anatomical development and progression of thyroid-associated ophthalmopathy (TAO). To develop a semantic segmentation method to extract the total EOM and ON from orbital CT images in patients with suspected TAO. A total of 7879 images obtained from 97 subjects were enrolled in this study. 88 patients were randomly selected into the training/validation dataset, and the rest were put into the test dataset. Contours of the total EOM and ON in all patients were manually delineated by experienced radiologists as the ground truth. A three-dimensional(3D) end-to-end fully convolutional neural network called semantic V-net (SV-net) was developed for our segmentation task. Intersection over Union (IoU) was measured to evaluate the accuracy of the segmentation results, and Pearson correlation analysis was for evaluating the volumes measured from our segmentation results against those from the ground truth. It achieved an overall IoU of 0.8207 for the test dataset; the IoU was 0.7599 for the superior rectus muscle, 0.8183 for the lateral rectus muscle, 0.8481 for the medial rectus muscle, 0.8436 for the inferior rectus muscle and 0.8337 for the optic nerve. The volumes measured from our segmentation results agreed well with those from the ground truth(all R > 0.98, P < 0.0001). The qualitative and quantitative evaluations demonstrate excellent performance of our method in automatically extracting the total EOM and ON and measuring their volumes in orbital CT images. There is a great promise for clinical application to assess these anatomical structures for the diagnosis and prognosis of TAO.

Publication Title

Optik

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