Dual-stage deep learning framework for pigment epithelium detachment segmentation in polypoidal choroidal vasculopathy.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5611923/

Biomed Opt Express. 2017 Aug 10;8(9):4061-4076. doi: 10.1364/BOE.8.004061. eCollection 2017 Sep 1.

Dual-stage deep learning framework for pigment epithelium detachment segmentation in polypoidal choroidal vasculopathy.

Xu Y1, Yan K2, Kim J2, Wang X2, Li C2, Su L1, Yu S1, Xu X1, Feng DD2.

Author information

Abstract

Worldwide, polypoidal choroidal vasculopathy (PCV) is a common vision-threatening exudative maculopathy, and pigment epithelium detachment (PED) is an important clinical characteristic. Thus, precise and efficient PED segmentation is necessary for PCV clinical diagnosis and treatment. We propose a dual-stage learning framework via deep neural networks (DNN) for automated PED segmentation in PCV patients to avoid issues associated with manual PED segmentation (subjectivity, manual segmentation errors, and high time consumption).The optical coherence tomography scans of fifty patients were quantitatively evaluated with different algorithms and clinicians. Dual-stage DNN outperformed existing PED segmentation methods for all segmentation accuracy parameters, including true positive volume fraction (85.74 ± 8.69%), dice similarity coefficient (85.69 ± 8.08%), positive predictive value (86.02 ± 8.99%) and false positive volume fraction (0.38 ± 0.18%). Dual-stage DNN achieves accurate PED quantitative information, works with multiple types of PEDs and agrees well with manual delineation, suggesting that it is a potential automated assistant for PCV management.

KEYWORDS:

(100.0100) Image processing; (100.4996) Pattern recognition, neural networks; (110.4500) Optical coherence tomography; (170.3880) Medical and biological imaging

PMID:

 

28966847

 

PMCID:

 

PMC5611923

 

DOI:

 

10.1364/BOE.8.004061