期刊
JOURNAL OF DIGITAL IMAGING
卷 34, 期 3, 页码 691-704出版社
SPRINGER
DOI: 10.1007/s10278-021-00459-w
关键词
Age-related macular degeneration (AMD); Intra-retinal fluid (IRF); Subretinal fluid (SRF); Pigment epithelial detachment (PED); Retinal edema; Spectral-domain optical coherence tomography (SD-OCT)
资金
- National Natural Science Foundation of China [61671242, 61701222]
- Key R&D Program of Jiangsu Provincial Department of Science and Technology [BE2018131]
This study introduces an improved CNN architecture called RetFluidNet to segment fluid abnormalities in AMD from SD-OCT images. The model integrates various pooling and connection operations to achieve best performance, showing significant improvement in accuracy and time efficiency.
Age-related macular degeneration (AMD) is one of the leading causes of irreversible blindness and is characterized by fluid-related accumulations such as intra-retinal fluid (IRF), subretinal fluid (SRF), and pigment epithelial detachment (PED). Spectral-domain optical coherence tomography (SD-OCT) is the primary modality used to diagnose AMD, yet it does not have algorithms that directly detect and quantify the fluid. This work presents an improved convolutional neural network (CNN)-based architecture called RetFluidNet to segment three types of fluid abnormalities from SD-OCT images. The model assimilates different skip-connect operations and atrous spatial pyramid pooling (ASPP) to integrate multi-scale contextual information; thus, achieving the best performance. This work also investigates between consequential and comparatively inconsequential hyperparameters and skip-connect techniques for fluid segmentation from the SD-OCT image to indicate the starting choice for future related researches. RetFluidNet was trained and tested on SD-OCT images from 124 patients and achieved an accuracy of 80.05%, 92.74%, and 95.53% for IRF, PED, and SRF, respectively. RetFluidNet showed significant improvement over competitive works to be clinically applicable in reasonable accuracy and time efficiency. RetFluidNet is a fully automated method that can support early detection and follow-up of AMD.
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