Chen Wen, Yu Xiangle, Ye Yiru, Gao Hebei, Cao Xinyuan, Lin Guangqing, Zhang Riyan, Li Zixuan, Wang Xinmin, Zhou Yuheng, Shen Meixiao, Shao Yilei
School of Ophthalmology and Optometry, Wenzhou Medical University, Wenzhou, China.
The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Ther Adv Chronic Dis. 2023 Mar 14;14:20406223231159616. doi: 10.1177/20406223231159616. eCollection 2023.
The ciliary muscle plays a role in changing the shape of the crystalline lens to maintain the clear retinal image during near work. Studying the dynamic changes of the ciliary muscle during accommodation is necessary for understanding the mechanism of presbyopia. Optical coherence tomography (OCT) has been frequently used to image the ciliary muscle and its changes during accommodation . However, the segmentation process is cumbersome and time-consuming due to the large image data sets and the impact of low imaging quality.
This study aimed to establish a fully automatic method for segmenting and quantifying the ciliary muscle on the basis of optical coherence tomography (OCT) images.
A perspective cross-sectional study.
In this study, 3500 signed images were used to develop a deep learning system. A novel deep learning algorithm was created from the widely used U-net and a full-resolution residual network to realize automatic segmentation and quantification of the ciliary muscle. Finally, the algorithm-predicted results and manual annotation were compared.
For segmentation performed by the system, the total mean pixel value difference (PVD) was 1.12, and the Dice coefficient, intersection over union (IoU), and sensitivity values were 93.8%, 88.7%, and 93.9%, respectively. The performance of the system was comparable with that of experienced specialists. The system could also successfully segment ciliary muscle images and quantify ciliary muscle thickness changes during accommodation.
We developed an automatic segmentation framework for the ciliary muscle that can be used to analyze the morphological parameters of the ciliary muscle and its dynamic changes during accommodation.
睫状肌在近距工作时通过改变晶状体形状来维持视网膜图像清晰。研究调节过程中睫状肌的动态变化对于理解老花眼机制很有必要。光学相干断层扫描（OCT）已被频繁用于对睫状肌及其调节过程中的变化进行成像。然而，由于图像数据集庞大以及成像质量较低的影响，分割过程繁琐且耗时。
本研究旨在基于光学相干断层扫描（OCT）图像建立一种全自动的睫状肌分割和量化方法。
一项前瞻性横断面研究。
本研究使用3500张已签署同意的图像来开发深度学习系统。从广泛使用的U-net和全分辨率残差网络创建了一种新颖的深度学习算法，以实现睫状肌的自动分割和量化。最后，将算法预测结果与人工标注进行比较。
对于系统进行的分割，总平均像素值差异（PVD）为1.12，Dice系数、交并比（IoU）和敏感度值分别为93.8%、88.7%和93.9%。该系统的性能与经验丰富的专家相当。该系统还能成功分割睫状肌图像并量化调节过程中睫状肌厚度的变化。
我们开发了一种用于睫状肌的自动分割框架，可用于分析睫状肌的形态学参数及其在调节过程中的动态变化。
