  Objectives: Precise segmentation of total extraocular muscles (EOM) and optic
nerve (ON) is essential to assess anatomical development and progression of
thyroid-associated ophthalmopathy (TAO). We aim to develop a semantic
segmentation method based on deep learning to extract the total EOM and ON from
orbital CT images in patients with suspected TAO. Materials and Methods: A
total of 7,879 images obtained from 97 subjects who underwent orbit CT scans
due to suspected TAO were enrolled in this study. Eighty-eight 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 the 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 used to evaluate the volumes
measured from our segmentation results against those from the ground truth.
Results: Our model in the test dataset achieved an overall IoU of 0.8207; 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). Conclusion: 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.
