Deep learning algorithms to segment and quantify the choroidal thickness and vasculature in swept-source optical coherence tomography images
Deep learning algorithms to segment and quantify the choroidal thickness and vasculature in swept-source optical coherence tomography images
复制标题
用于分割和量化扫频光学相干断层扫描图像中脉络膜厚度和脉管系统的深度学习算法
DOI：
10.1142/s1793545821400022
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发表时间：
2020-11
影响因子：
2.5
通讯作者：
Meixiao Shen
中科院分区：
文献类型：
--
作者：
Gu Zheng;Yanfeng Jiang;Ce Shi;Hanpei Miao;Xiangle Yu;Yiyi Wang;Sisi Chen;Zhiyang Lin;Weicheng Wang;Fan Lu;Meixiao Shen
准确分割脉络膜厚度（CT）和脉络膜血管对更好地分析和了解脉络膜相关眼部疾病具有重要意义。本文提出并实现了一种新颖实用的基于深度学习算法残差U-Net的CT和血管自动分割和量化方法。在训练数据和验证数据有限的情况下，与经验丰富的操作员相比，残差U-Net能够像人工分割一样精确地识别脉络膜边界。然后，将训练好的深度学习算法应用于217幅图像，提取了6个脉络膜相关参数，发现手动和自动分割方法的类内相关系数（ICC）均大于0.964。自动分割方法的重现性也很好，ICC均大于0.913，说明自动分割方法的一致性较好。结果表明，深度学习算法可以准确有效地分割脉络膜边界，有助于CT和脉管系统的量化。
Accurate segmentation of choroidal thickness (CT) and vasculature is important to better analyze and understand the choroid-related ocular diseases. In this paper, we proposed and implemented a novel and practical method based on the deep learning algorithms, residual U-Net, to segment and quantify the CT and vasculature automatically. With limited training data and validation data, the residual U-Net was capable of identifying the choroidal boundaries as precise as the manual segmentation compared with an experienced operator. Then, the trained deep learning algorithms was applied to 217 images and six choroidal relevant parameters were extracted, we found high intraclass correlation coefficients (ICC) of more than 0.964 between manual and automatic segmentation methods. The automatic method also achieved great reproducibility with ICC greater than 0.913, indicating good consistency of the automatic segmentation method. Our results suggested the deep learning algorithms can accurately and efficiently segment choroid boundaries, which will be helpful to quantify the CT and vasculature.
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影响因子：
4.4
作者：
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通讯作者：
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