High-accuracy 3D segmentation of wet age-related macular degeneration via multi-scale and cross-channel feature extraction and channel attention

Meixuan Li; Yadan Shen; Renxiong Wu; Shaoyan Huang; Fei Zheng; Sizhu Chen; Rong Wang; Wentao Dong; Jie Zhong; Guangming Ni; and Yong Liu

High-accuracy 3D segmentation of wet age-related macular degeneration via multi-scale and cross-channel feature extraction and channel attention

Author : Meixuan Li, Yadan Shen, Renxiong Wu, Shaoyan Huang, Fei Zheng, Sizhu Chen, Rong Wang, Wentao Dong, Jie Zhong, Guangming Ni, and Yong Liu Journa Name: BIOMEDICAL OPTICS EXPRESS Country : USA Volume: 15 issue: 2 Year: 2024 Views : 242

Abstract:

Wet age-related macular degeneration (AMD) is the leading cause of visual impairment and vision loss in the elderly, and optical coherence tomography (OCT) enables revolving biotissue three-dimensional micro-structure widely used to diagnose and monitor wet AMD lesions. Many wet AMD segmentation methods based on deep learning have achieved good results, but these segmentation results are two-dimensional, and cannot take full advantage of OCT's three-dimensional (3D) imaging characteristics. Here we propose a novel deep-learning network characterizing multi-scale and cross-channel feature extraction and channel attention to obtain high-accuracy 3D segmentation results of wet AMD lesions and show the 3D specific morphology, a task unattainable with traditional two-dimensional segmentation. This probably helps to understand the ophthalmologic disease and provides great convenience for the clinical diagnosis and treatment of wet AMD.