FARGO: A Joint Framework for FAZ and RV Segmentation from OCTA Images

Peng，Linkai1; Lin，Li1,2; Cheng，Pujin1; Wang，Zhonghua1; Tang，Xiaoying1

10.1007/978-3-030-87000-3_5

2021

0302-9743

1611-3349

LECT NOTES ARTIF INT

12970 LNCS

42-51

Optical coherence tomography angiography (OCTA) is a recent advance in ophthalmic imaging, which provides detailed visualization of two important anatomical landmarks, namely foveal avascular zone (FAZ) and retinal vessels (RV). Studies have shown that both FAZ and RV play significant roles in the diagnoses of various eye-related diseases. Therefore, accurate segmentation of FAZ and RV from OCTA images is highly in need. However, due to complicated microstructures and inhomogeneous image quality, there is still room for improvement in existing methods. In this paper, we propose a novel and efficient deep learning framework containing two subnetworks for simultaneously segmenting FAZ and RV from en-face OCTA images, named FARGO. For FAZ, we use RV segmentation as an auxiliary task, which may provide supplementary information especially for low-contrast and low-quality OCTA images. A ResNeSt based encoder with split attention and ImageNet pretraining is employed for FAZ segmentation. For RV, we introduce a coarse-to-fine cascaded network composed of a main segmentation model and several small ones for progressive refining. Spatial attention and channel attention modules are utilized for adaptively integrating local features with global dependencies. Through extensive experiments, FARGO is found to yield outstanding segmentation results for both FAZ and RV on the OCTA-500 dataset, performing even better than methods that utilize 3D OCTA volume as an extra input.

Coarse-to-fine Foveal avascular zone Joint segmentation OCTA ResNeSt Retinal vessels

第一 ; 通讯

英语

EI

20213910959258

Deep learning ; Diagnosis ; Image enhancement ; Image segmentation ; Medical computing ; Medical imaging ; Ophthalmology

Biomedical Engineering:461.1 ; Ergonomics and Human Factors Engineering:461.4 ; Medicine and Pharmacology:461.6 ; Computer Applications:723.5 ; Optical Devices and Systems:741.3 ; Imaging Techniques:746

2-s2.0-85115875301

Scopus

1.Department of Electrical and Electronic Engineering,Southern University of Science and Technology,Shenzhen,China
2.Department of Electrical and Electronic Engineering,The University of Hong Kong,Hong Kong

Peng，Linkai,Lin，Li,Cheng，Pujin,et al. FARGO: A Joint Framework for FAZ and RV Segmentation from OCTA Images[C],2021:42-51.