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. 2019 Jan 14;10(2):526-538.
doi: 10.1364/BOE.10.000526. eCollection 2019 Feb 1.

Region-segmentation strategy for Bruch's membrane opening detection in spectral domain optical coherence tomography images

Zailiang Chen  1 , Peng Peng  1 , Hailan Shen  1 , Hao Wei  1 , Pingbo Ouyang  2 , Xuanchu Duan  3
Affiliations

Region-segmentation strategy for Bruch's membrane opening detection in spectral domain optical coherence tomography images

Zailiang Chen et al. Biomed Opt Express. .

Abstract

Bruch's membrane opening (BMO) is an important biomarker in the progression of glaucoma. Bruch's membrane opening minimum rim width (BMO-MRW), cup-to-disc ratio in spectral domain optical coherence tomography (SD-OCT) and lamina cribrosa depth based on BMO are important measurable parameters for glaucoma diagnosis. The accuracy of measuring these parameters is significantly affected by BMO detection. In this paper, we propose a method for automatically detecting BMO in SD-OCT volumes accurately to reduce the impact of the border tissue and vessel shadows. The method includes three stages: a coarse detection stage composed by retinal pigment epithelium layer segmentation, optic disc segmentation, and multi-modal registration; a fixed detection stage based on the U-net in which BMO detection is transformed into a region segmentation problem and an area bias component is proposed in the loss function; and a post-processing stage based on the consistency of results to remove outliers. Experimental results show that the proposed method outperforms previous methods and achieves a mean error of 42.38 μm.

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Conflict of interest statement

The authors declare that there are no conflicts of interest related to this article.

Figures

Fig. 1
Fig. 1
Flowchart of the proposed method.
Fig. 2
Fig. 2
Coarse detection. (a) Original SD-OCT images and 2D projection images. (b) Registration between the projection and fundus images. (c) Diagram of the coarse detection, intersection (yellow) (yellow) of the RPE layer (red), and projection line of the disc (green) are the coarse detection results.
Fig. 3
Fig. 3
Data process.(a) ROI determination. (b) Label transformation for different patches.
Fig. 4
Fig. 4
U-net architecture in our proposed method.
Fig. 5
Fig. 5
Illustration of the border tissue and the post-processing steps. (a) The sketch map of ambiguous border tissue. The real BMO is confused by the border tissue in this case. (b) The sketch map of post-processing. There are four patches (the box in the ROI indicate the upper left patch) extracted in the ROI and send to the trained U-net, the mask of patches with maximum Mi is selected as the result and its center point is the final result of BMO.
Fig. 6
Fig. 6
Results of various radius parameters.
Fig. 7
Fig. 7
Result of various loss function components. (a) Results of group 4 (green), group 2 (yellow), and the ground truth (red). (b) Results of group 4 (green), group 3 (pink), and the ground truth (red). Best viewed in color.
Fig. 8
Fig. 8
Evaluation of the effect of post-processing. The dots in red, green, and yellow represent the ground truth, the result with post-processing, and the result without post-processing, respectively. (a) Result of the method without post-processing is satisfactory. (b) Result of the method without post-processing has a large deviation. Best viewed in color.
Fig. 9
Fig. 9
Comparison of our proposed method (yellow) with the ground truth (red) in the 2D projection image.
See this image and copyright information in PMC

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