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Automatic Segmentation and Measurement of Choroid Layer in High Myopia for OCT Imaging Using Deep Learning

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Abstract

Automatic segmentation and measurement of the choroid layer is useful in studying of related fundus diseases, such as diabetic retinopathy and high myopia. However, most algorithms are not helpful for choroid layer segmentation due to its blurred boundaries and complex gradients. Therefore, this paper aimed to propose a novel choroid segmentation method that combines image enhancement and attention-based dense (AD) U-Net network. The choroidal images obtained from optical coherence tomography (OCT) are pre-enhanced by algorithms that include flattening, filtering, and exponential and linear enhancement to reduce choroid-independent information. Experimental results obtained from 800 OCT B-scans of the choroid layers from both normal eyes and high myopia showed that image enhancement significantly increased the performance of ADU-Net, with an AUC of 99.51% and a DSC of 97.91%. The accuracy of segmentation using the ADU-Net method with image enhancement is superior to that of the existing networks. In addition, we describe some algorithms that can measure automatically choroidal foveal thickness and the volume of adjacent areas. Statistical analyses of the choroidal parameters variation indicated that compared with normal eyes, high myopia has a reduction of 86.3% of the choroidal foveal thickness and 90% of the adjacent volume. It proved that high myopia is likely to cause choroid layer attenuation. These algorithms would have wide application in the diagnosis and precaution of related fundus lesions caused by choroid thinning from high myopia in future studies.

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Availability of Data and Material

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

Code Availability

The code that support the findings of this study are available from the corresponding author upon reasonable request.

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Funding

This work was supported by Key-Area Research and Development Program of Guangdong Province (No.2020B1111040001), National Natural Science Foundation of China (NSFC) (No. 61805038, 62075042, 61705036, 61771139), and Research Fund of Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology (No. 2020B1212030010).

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Author notes

  1. Xiangcong Xu and Xuehua Wang contributed equally to this work.

Authors and Affiliations

  1. School of Physics and Optoelectronic Engineering, Foshan University, Foshan, Guangdong, China

    Xiangcong Xu, Xuehua Wang, Jingyi Lin, Honglian Xiong, Mingyi Wang, Haishu Tan & Dingan Han

  2. Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic, Foshan, People’s Republic of China

    Xiangcong Xu, Xuehua Wang, Jingyi Lin, Honglian Xiong, Mingyi Wang, Haishu Tan & Dingan Han

  3. School of Mechatronic Engineering and Automation, Foshan University, Foshan, Guangdong, China

    Xiangcong Xu

  4. Department of Ophthalmology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China

    Ke Xiong

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  1. Xiangcong Xu
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Contributions

Xiangcong Xu, Xuehua Wang, Ke Xiong, and Dingan Han designed the study. Xuehua Wang, Xiangcong Xu, and Jingyi Lin wrote the manuscript. Xiangcong Xu wrote the algorithm and performed the experiments. All the authors planned experiments and contributed to writing the paper.

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Correspondence to Xuehua Wang, Ke Xiong or Dingan Han.

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Xu, X., Wang, X., Lin, J. et al. Automatic Segmentation and Measurement of Choroid Layer in High Myopia for OCT Imaging Using Deep Learning. J Digit Imaging 35, 1153–1163 (2022). https://doi.org/10.1007/s10278-021-00571-x

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