Open AccessPhase unwrapping based on a residual en-decoder network for phase images in Fourier domain Doppler optical coherence tomography
Author(s) -
Chuanchao Wu,
Zhengyu Qiao,
Nan Zhang,
Xiaochen Li,
Jingfan Fan,
Hong Song,
Danni Ai,
Jian Yang,
Yong Huang
Publication year - 2020
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.386101
Subject(s) - computer science , optical coherence tomography , artificial intelligence , residual , image processing , computer vision , robustness (evolution) , pixel , fourier transform , phase noise , optics , pattern recognition (psychology) , algorithm , mathematics , image (mathematics) , mathematical analysis , biochemistry , chemistry , physics , gene
To solve the phase unwrapping problem for phase images in Fourier domain Doppler optical coherence tomography (DOCT), we propose a deep learning-based residual en-decoder network (REDN) method. In our approach, we reformulate the definition for obtaining the true phase as obtaining an integer multiple of 2 π at each pixel by semantic segmentation. The proposed REDN architecture can provide recognition performance with pixel-level accuracy. To address the lack of phase images that are noise and wrapping free from DOCT systems for training, we used simulated images synthesized with DOCT phase image background noise features. An evaluation study on simulated images, DOCT phase images of phantom milk flowing in a plastic tube and a mouse artery, was performed. Meanwhile, a comparison study with recently proposed deep learning-based DeepLabV3+ and PhaseNet methods for signal phase unwrapping and traditional modified networking programming (MNP) method was also performed. Both visual inspection and quantitative metrical evaluation based on accuracy, specificity, sensitivity, root-mean-square-error, total-variation, and processing time demonstrate the robustness, effectiveness and superiority of our method. The proposed REDN method will benefit accurate and fast DOCT phase image-based diagnosis and evaluation when the detected phase is wrapped and will enrich the deep learning-based image processing platform for DOCT images.