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. 2020 Apr 13;9(2):21.
doi: 10.1167/tvst.9.2.21. eCollection 2020 Apr.

Fast and Automated Hyperreflective Foci Segmentation Based on Image Enhancement and Improved 3D U-Net in SD-OCT Volumes with Diabetic Retinopathy

Sha Xie  1 , Idowu Paul Okuwobi  1 , Mingchao Li  1 , Yuhan Zhang  1 , Songtao Yuan  2 , Qiang Chen  1
Affiliations

Fast and Automated Hyperreflective Foci Segmentation Based on Image Enhancement and Improved 3D U-Net in SD-OCT Volumes with Diabetic Retinopathy

Sha Xie et al. Transl Vis Sci Technol. .

Abstract

Purpose: To design a robust and automated hyperreflective foci (HRF) segmentation framework for spectral-domain optical coherence tomography (SD-OCT) volumes, especially volumes with low HRF-background contrast.

Methods: HRF in retinal SD-OCT volumes appear with low-contrast characteristics that results in the difficulty of HRF segmentation. Therefore to effectively segment the HRF we proposed a fully automated method for HRF segmentation in SD-OCT volumes with diabetic retinopathy (DR). First, we generated the enhanced SD-OCT images from the denoised SD-OCT images with an enhancement method. Then the enhanced images were cascaded with the denoised images as the two-channel input to the network against the low-contrast HRF. Finally, we replaced the standard convolution with slice-wise dilated convolution in the last layer of the encoder path of 3D U-Net to obtain long-range information.

Results: We evaluated our method using two-fold cross-validation on 33 SD-OCT volumes from 27 patients. The average dice similarity coefficient was 70.73%, which was higher than that of the existing methods with significant difference (P < 0.01).

Conclusions: Experimental results demonstrated that the proposed method is faster and achieves more reliable segmentation results than the current HRF segmentation algorithms. We expect that this method will contribute to clinical diagnosis and disease surveillance.

Translational relevance: Our framework for the automated HRF segmentation of SD-OCT volumes may improve the clinical diagnosis of DR.

Keywords: 3D U-Net; SD-OCT; hyperreflective foci segmentation; image enhancement; slice-wise dilated convolution.

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Conflict of interest statement

Disclosure: S. Xie, None; I.P. Okuwobi, None; M. Li, None; Y. Zhang, None; S. Yuan, None; Q. Chen, None

Figures

Figure 1.
Figure 1.
(a) One B-scan of an SD-OCT volume. The HRF are located between the NFL/GCL and the IS/OS. (b) Scaled-up local region with HRF. The HRF have high and nonuniform intensities, irregular shapes, and varying sizes. (c) Scaled-up local region with HRF. The HRF have blurry boundaries. Two retinal layers (NFL/GCL and IS/OS) are marked with yellow arrows, and HRF are marked with red arrows.
Figure 2.
Figure 2.
(a) One B-scan with high HRF-background contrast. (b) One B-scan with low HRF-background contrast. HRF are marked with red arrows.
Figure 3.
Figure 3.
Overview of the proposed method.
Figure 4.
Figure 4.
Pipeline of the enhancement algorithm.
Figure 5.
Figure 5.
(a) Raw image. (b) Denoised image. (c) Enhanced image.
Figure 6.
Figure 6.
Segmentation results using different input to our network. Yellow arrows represent the regions of undersegmentation.
Figure 7.
Figure 7.
Segmentation results using our input to different network. Yellow arrows represent the regions of undersegmentation.
Figure 8.
Figure 8.
Comparison between the proposed method and other methods. Yellow and green arrows represent the regions of undersegmentation and oversegmentation, respectively.
Figure 9.
Figure 9.
Examples of the oversegmentations with our method. (a) Oversegmentation caused by edemas, (b) oversegmentation caused by vessels.
Figure 10.
Figure 10.
Comparison of the results on 33 eyes obtained by our network with different input.
See this image and copyright information in PMC

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