CGNet-assisted Automatic Vessel Segmentation for Optical Coherence Tomography Angiography

Yu，Xiaojun1,2; Ge，Chenkun1; Aziz，Muhammad Zulkifal1; Li，Mingshuai1; Shum，Perry Ping3; Liu，Linbo4; Mo，Jianhua5

2022

10.1002/jbio.202200067

Journal of Biophotonics   Impact Factor & Quartile

1864-063X

1864-0648

Automatic optical coherence tomography angiography (OCTA) vessel segmentation is of great significance to retinal disease diagnoses. Due to the complex vascular structure, however, various existing factors make the segmentation task challenging. This paper reports a novel end-to-end three-stage channel and position attention (CPA) module integrated graph reasoning convolutional neural network (CGNet) for retinal OCTA vessel segmentation. Specifically, in the coarse stage, both CPA and graph reasoning network (GRN) modules are integrated in between a U-shaped neural network encoder and decoder to acquire vessel confidence maps. After being directed into a fine stage, such confidence maps are concatenated with the original image and the generated fine image map as a 3-channel image to refine retinal micro-vasculatures. Finally, both the fine and refined images are fused at the refining stage as the segmentation results. Experiments with different public datasets are conducted to verify the efficacy of the proposed CGNet. Results show that by employing the end-to-end training scheme and the integrated CPA and GRN modules, CGNet achieves 94.29% and 85.62% in area under the ROC curve (AUC) for the two different datasets, outperforming the state-of-the-art existing methods with both improved operability and reduced complexity in different cases. Code is available at https://github.com/GE-123-cpu/CGnet-for-vessel-segmentation.

convolutional neural network optical coherence tomography angiography (OCTA) training scheme vessel segmentation

SCI ; EI

English

Others

WOS:000821530200001

20222812334950

Angiography ; Complex networks ; Convolution ; Image segmentation ; Network coding ; Ophthalmology ; Optical tomography

Medicine and Pharmacology:461.6 ; Information Theory and Signal Processing:716.1 ; Computer Systems and Equipment:722 ; Optical Devices and Systems:741.3 ; Imaging Techniques:746

2-s2.0-85133514415

Scopus

1.School of Automation,Northwestern Polytechnical University,Xi'an,China
2.Shenzhen Research Institute of Northwestern Polytechnical University,Shenzhen,Guangdong,China
3.Department of Electrical and Electronic Engineering,Southern University of Science and Technology,Shenzhen,China
4.School of Electrical and Electronic Engineering,Nanyang Technological University,Singapore,Singapore
5.School of Electronics and Information Engineering,Soochow University,Suzhou,China

Yu，Xiaojun,Ge，Chenkun,Aziz，Muhammad Zulkifal,et al. CGNet-assisted Automatic Vessel Segmentation for Optical Coherence Tomography Angiography[J]. Journal of Biophotonics,2022.

Yu，Xiaojun.,Ge，Chenkun.,Aziz，Muhammad Zulkifal.,Li，Mingshuai.,Shum，Perry Ping.,...&Mo，Jianhua.(2022).CGNet-assisted Automatic Vessel Segmentation for Optical Coherence Tomography Angiography.Journal of Biophotonics.

Yu，Xiaojun,et al."CGNet-assisted Automatic Vessel Segmentation for Optical Coherence Tomography Angiography".Journal of Biophotonics (2022).