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Recent Advanced Deep Learning Architectures for Retinal Fluid Segmentation on Optical Coherence Tomography Images

期刊

SENSORS
卷 22, 期 8, 页码 -

出版社

MDPI
DOI: 10.3390/s22083055

关键词

optical coherence tomography; machine learning; neural networks; retinal fluid segmentation; ophthalmic diseases

资金

  1. Fundamental Research Funds for the Central Universities of China [20720200033]
  2. Fujian Provincial Innovation Strategy Research Project [2021R0001]

向作者/读者索取更多资源

Optical coherence tomography (OCT) is a widely used retinal imaging modality for diagnosing ophthalmic diseases, and using deep learning for automatic retinal fluid segmentation can improve accuracy and efficiency. This article summarizes different deep learning methods and OCT datasets used for retinal fluid segmentation.
With non-invasive and high-resolution properties, optical coherence tomography (OCT) has been widely used as a retinal imaging modality for the effective diagnosis of ophthalmic diseases. The retinal fluid is often segmented by medical experts as a pivotal biomarker to assist in the clinical diagnosis of age-related macular diseases, diabetic macular edema, and retinal vein occlusion. In recent years, the advanced machine learning methods, such as deep learning paradigms, have attracted more and more attention from academia in the retinal fluid segmentation applications. The automatic retinal fluid segmentation based on deep learning can improve the semantic segmentation accuracy and efficiency of macular change analysis, which has potential clinical implications for ophthalmic pathology detection. This article summarizes several different deep learning paradigms reported in the up-to-date literature for the retinal fluid segmentation in OCT images. The deep learning architectures include the backbone of convolutional neural network (CNN), fully convolutional network (FCN), U-shape network (U-Net), and the other hybrid computational methods. The article also provides a survey on the prevailing OCT image datasets used in recent retinal segmentation investigations. The future perspectives and some potential retinal segmentation directions are discussed in the concluding context.

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