###### Abstract  
The purpose of this article is to evaluate the accuracy of the optical coherence tomography (OCT) measurement of choroidal thickness in healthy eyes using a deep-learning method with the Mask R-CNN model. Thirty EDI-OCT of thirty patients were enrolled. A mask region-based convolutional neural network (Mask R-CNN) model composed of deep residual network (ResNet) and feature pyramid networks (FFNs) with standard convolution and fully connected heads for mask and box prediction, respectively, was used to automatically depict the choroid layer. The average choroidal thickness and subfoveal choroidal thickness were measured. The results of this study showed that ResNet 50 layers deep (R50) model and ResNet 101 layers deep (R101). R101 U R50 (OR model) demonstrated the best accuracy with an average error of 4.85 pixels and 4.86 pixels, respectively. The R101 \(\cap\) R50 (AND model) took the least time with an average execution time of 4.6 s. Mask-RCNN models showed a good prediction rate of choroidal layer with accuracy rates of 90% and 89.9% for average choroidal thickness and average subfoveal choroidal thickness, respectively. In conclusion, the deep-learning method using the Mask-RCNN model provides a faster and accurate measurement of choroidal thickness. Comparing with manual delineation, it provides better effectiveness, which is feasible for clinical application and larger scale of research on choroid. m 2021 11 5488. [[https://doi.org/10.3390/app11125488](https://doi.org/10.3390/app11125488)]([https://doi.org/10.3390/app11125488](https://doi.org/10.3390/app11125488))  
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[PERSON]; [PERSON]; [PERSON]; [PERSON] Automatic Segmentation of Choroid Layer Using Deep Learning on Spectral Domain Optical Coherence Tomography. _Appl. Sci._**2021**, _11_, 5488. [[https://doi.org/10.3390/app11125488](https://doi.org/10.3390/app11125488)]([https://doi.org/10.3390/app11125488](https://doi.org/10.3390/app11125488))  
Academic Editor: [PERSON]  
Received: 9 May 2021  
Accepted: 11 June 2021  
Subscribed: 13 June 2021  
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**Published: 13 June 2021**
such as age, pupil size, cataract and vitreous opacity [4]. Hence, in the development of automatic segmentation of the choroid boundary, difficult clinical situations, such as abnormal ocular anatomy and nonignorable noise, may be encountered. Various approaches [5; 6] have been proposed for segmentation procedures of automated choroid layer segmentation in OCT images of the posterior visual section. However, [PERSON] et al. propose a framework only for internal limiting membrane (ILM) and choroidal inner boundary (CiB) of segmentation [5]. For the choroidal outer boundary (CoB), segmentation should be more difficult than ILM and CiB. Since there are numerous issues such as the heterogenous texture of tissues, artifact speckles and boundary discontinuities, which often lead to difficulties in extracting accurate boundaries of the choroid layer in OCT imaging, the CoB blur extent will be different. Although the CNN classifier and l2-lq (0 < q < 1) fitter model proposed by [PERSON] et al. is able to depict Cob, its structure consists of the framework of LeNet-5, which is part of the traditional convolutional neural networks (CNN) [6]. The LeNet-5 convolution layers have 15 layers and an extract feature only in shallow. When compared to the mask region-based convolutional neural network (Mask R-CNN), which combines shallow and deep layer features, LeNet-5 might lose critically important location information. Figure 1a shows an original OCT image with a contrast-enhanced area (the dotted rectangle) that includes various noises and an artifact speckle. Figure 1b shows the proposed method that segments the OCT image into three layers, namely, the ILM, CiB, and CoB. Segmentation procedures are complicated and especially time-consuming.