Ocular Rectus Muscle Segmentation Based on Improved U-net. (May 2019)

• Record Type: Journal Article
• Authors: Wu, Cong; Zhan, Jinhao; Zou, Yixuan; Jiang, Fagang; Yang, Junjie
• Is Part Of: IOP conference series. Volume 533(2019)
• Publication Date: 2019-05
• Subjects: Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/533/1/012058
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: In recent years, deep learning has made great progress in computer vision, and shown a good application prospect in reading medical images. TAO (thyroid-associated ophthalmopathy) is one of the most common orbital diseases in adults is autoimmune disease, the exact pathogenesis is not clear. In hospitals, CT images are usually used for disease analysis and the distortion of ocular rectus muscles is one of the main causes of TAO, thus the separation of the ocular rectus muscle is of great practical significance. However, the artificial segmentation of the ocular rectus muscle is a time-consuming task and relies heavily on the experience of the operato. In this paper, we improved the traditional U-net with GoogLeNet inception module and apply it on the segmentation of ocular rectus muscles. The experimental results show that the segmentation results of our method are more accurate and efficient than the traditional algorithm, and it is helpful for doctors to diagnose patients' diseases.