Ocular Rectus Muscle Segmentation Based on Improved U-net

2019 The 5th International Conference on Electrical Engineering, Control and Robotics

无线电电子学;计算机科学

Wu, Cong^1 ; Zhan, Jinhao^1 ; Zou, Yixuan^1 ; Jiang, Fagang^1 ; Yang, Junjie^1

Hubei University of Techonology, Nanli Road, Hong-shan District, Hubei Wuchang, Wuhan, China^1

关键词: Application prospect;    Autoimmune disease;    CT Image;    Disease analysis;    Muscle segmentation;    Segmentation results;    Time-consuming tasks;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/533/1/012058/pdf
DOI  :  10.1088/1757-899X/533/1/012058

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

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.