Rong Yibiao, Jiang Zehua, Wu Weihang, Chen Qifeng, Wei Chuliang, Fan Zhun, Chen Haoyu
College of Engineering, Shantou University, Shantou 515063, China.
Key Laboratory of Digital Signal and Image Processing of Guangdong Provincial, Shantou University, Shantou 515063, China.
J Clin Med. 2022 Jun 4;11(11):3203. doi: 10.3390/jcm11113203.
Automatic and accurate estimation of choroidal thickness plays a very important role in a computer-aided system for eye diseases. One of the most common methods for automatic estimation of choroidal thickness is segmentation-based methods, in which the boundaries of the choroid are first detected from optical coherence tomography (OCT) images. The choroidal thickness is then computed based on the detected boundaries. A shortcoming in the segmentation-based methods is that the estimating precision greatly depends on the segmentation results. To avoid the dependence on the segmentation step, in this paper, we propose a direct method based on convolutional neural networks (CNNs) for estimating choroidal thickness without segmentation. Concretely, a B-scan image is first cropped into several patches. A trained CNN model is then used to estimate the choroidal thickness for each patch. The mean thickness of the choroid in the B-scan is obtained by taking the average of the choroidal thickness on each patch. Then, 150 OCT volumes are collected to evaluate the proposed method. The experiments show that the results obtained by the proposed method are very competitive with those obtained by segmentation-based methods, which indicates that direct estimation of choroidal thickness is very promising.
脉络膜厚度的自动准确估计在眼部疾病的计算机辅助系统中起着非常重要的作用。自动估计脉络膜厚度最常用的方法之一是基于分割的方法，其中首先从光学相干断层扫描（OCT）图像中检测脉络膜的边界。然后根据检测到的边界计算脉络膜厚度。基于分割的方法的一个缺点是估计精度很大程度上取决于分割结果。为了避免依赖分割步骤，在本文中，我们提出了一种基于卷积神经网络（CNN）的直接方法，用于在不进行分割的情况下估计脉络膜厚度。具体来说，首先将B扫描图像裁剪成几个小块。然后使用经过训练的CNN模型来估计每个小块的脉络膜厚度。通过对每个小块上的脉络膜厚度取平均值来获得B扫描中脉络膜的平均厚度。然后，收集了150个OCT体积来评估所提出的方法。实验表明，所提出的方法获得的结果与基于分割的方法获得的结果具有很强的竞争力，这表明直接估计脉络膜厚度非常有前景。
