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Automatic detection of leakage point in central serous chorioretinopathy of fundus fluorescein angiography based on time sequence deep learning

  • Medical Ophthalmology
  • Published:
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Abstract

Purpose

To detect the leakage points of central serous chorioretinopathy (CSC) automatically from dynamic images of fundus fluorescein angiography (FFA) using a deep learning algorithm (DLA).

Methods

The study included 2104 FFA images from 291 FFA sequences of 291 eyes (137 right eyes and 154 left eyes) from 262 patients. The leakage points were segmented with an attention gated network (AGN). The optic disk (OD) and macula region were segmented simultaneously using a U-net. To reduce the number of false positives based on time sequence, the leakage points were matched according to their positions in relation to the OD and macula.

Results

With the AGN alone, the number of cases whose detection results perfectly matched the ground truth was only 37 out of 61 cases (60.7%) in the test set. The dice on the lesion level were 0.811. Using an elimination procedure to remove false positives, the number of accurate detection cases increased to 57 (93.4%). The dice on the lesion level also improved to 0.949.

Conclusions

Using DLA, the CSC leakage points in FFA can be identified reproducibly and accurately with a good match to the ground truth. This novel finding may pave the way for potential application of artificial intelligence to guide laser therapy.

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Funding

Supported by the National Key Research and Development Program of China (2019YFC0118401), Zhejiang Provincial Key Research and Development Plan (2019C03020), and the Natural Science Foundation of China (81670888). The authors alone are responsible for the content and writing of the paper.

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Author notes

  1. Menglu Chen and Kai Jin contributed equally to this work and share the first authorship.

Authors and Affiliations

  1. Department of Ophthalmology, the Second Affiliated Hospital of Zhejiang University, College of Medicine, Hangzhou, 310009, China

    Menglu Chen, Kai Jin, Yufeng Xu, Yao Wang & Juan Ye

  2. Hangzhou Truth Medical Technology Ltd, Hangzhou, 311215, China

    Kun You

  3. Department of Ophthalmology, Khoo Teck Puat Hospital, Yishun Central, Singapore

    Chee-Chew Yip

  4. College of Computer Science and Technology, Zhejiang University, Hangzhou, 310027, China

    Jian Wu

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  1. Menglu Chen
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Correspondence to Jian Wu or Juan Ye.

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This study was conducted in compliance with the principles of the Declaration of Helsinki and was approved by the Medical Ethics Committee of the Second Affiliated Hospital, Zhejiang University School of Medicine. Informed consent was obtained from the research subjects prior to the study.

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Chen, M., Jin, K., You, K. et al. Automatic detection of leakage point in central serous chorioretinopathy of fundus fluorescein angiography based on time sequence deep learning. Graefes Arch Clin Exp Ophthalmol 259, 2401–2411 (2021). https://doi.org/10.1007/s00417-021-05151-x

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