Cabeza-Gil Iulen, Ruggeri Marco, Chang Yu-Cherng, Calvo Begoña, Manns Fabrice
Aragón Institute of Engineering Research (i3A), University of Zaragoza, Zaragoza, Spain.
Ophthalmic Biophysics Center, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, USA.
Biomed Opt Express. 2022 Apr 21;13(5):2810-2823. doi: 10.1364/BOE.455661. eCollection 2022 May 1.
Quantifying shape changes in the ciliary muscle during accommodation is essential in understanding the potential role of the ciliary muscle in presbyopia. The ciliary muscle can be imaged using OCT but quantifying the ciliary muscle shape from these images has been challenging both due to the low contrast of the images at the apex of the ciliary muscle and the tedious work of segmenting the ciliary muscle shape. We present an automatic-segmentation tool for OCT images of the ciliary muscle using fully convolutional networks. A study using a dataset of 1,039 images shows that the trained fully convolutional network can successfully segment ciliary muscle images and quantify ciliary muscle thickness changes during accommodation. The study also shows that EfficientNet outperforms other current backbones of the literature.
量化调节过程中睫状肌的形状变化对于理解睫状肌在老花眼中的潜在作用至关重要。可以使用光学相干断层扫描(OCT)对睫状肌进行成像,但由于睫状肌顶端图像的对比度较低以及分割睫状肌形状的繁琐工作,从这些图像中量化睫状肌形状一直具有挑战性。我们提出了一种使用全卷积网络的睫状肌OCT图像自动分割工具。一项使用1039幅图像数据集的研究表明,经过训练的全卷积网络可以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。该研究还表明,EfficientNet优于文献中其他当前的骨干网络。