Open AccessRegion-segmentation strategy for Bruch’s membrane opening detection in spectral domain optical coherence tomography images
Author(s) -
Zailiang Chen,
Peng Peng,
Hailan Shen,
Hao Wei,
Pingbo Ouyang,
Xuanchu Duan
Publication year - 2019
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.10.000526
Subject(s) - optical coherence tomography , bruch's membrane , glaucoma , segmentation , artificial intelligence , computer science , computer vision , retinal pigment epithelium , image segmentation , optics , pattern recognition (psychology) , ophthalmology , physics , medicine , retina
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.