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

Area of Science:

• Ophthalmology and Medical Imaging
• Artificial Intelligence in Healthcare
• Biomedical Engineering

Background:

• Optical coherence tomography (OCT) is a high-resolution, non-invasive imaging technique crucial for diagnosing ophthalmic diseases.
• Retinal fluid segmentation is a key biomarker for diagnosing conditions such as age-related macular diseases, diabetic macular edema, and retinal vein occlusion.
• Manual segmentation by medical experts is time-consuming and subjective.

Purpose of the Study:

• To review and summarize various deep learning paradigms for automatic retinal fluid segmentation in OCT images.
• To highlight the potential of deep learning in enhancing the accuracy and efficiency of macular change analysis.
• To provide insights into current OCT image datasets and future research directions in retinal segmentation.

Main Methods:

• Survey of recent literature on deep learning architectures for retinal fluid segmentation.
• Inclusion of Convolutional Neural Networks (CNNs), Fully Convolutional Networks (FCNs), and U-Net architectures.
• Discussion of hybrid computational methods and prevailing OCT image datasets used in segmentation studies.

Main Results:

• Deep learning methods demonstrate significant improvements in semantic segmentation accuracy and efficiency for retinal fluid.
• Automatic segmentation aids in more precise analysis of macular changes, crucial for clinical diagnosis.
• Various deep learning architectures show promise for clinical applications in ophthalmic pathology detection.

Conclusions:

• Deep learning offers a powerful approach for automated retinal fluid segmentation in OCT imaging.
• Accurate segmentation facilitates earlier and more precise diagnosis of sight-threatening retinal diseases.
• Further research into advanced deep learning models and datasets will continue to refine retinal image analysis.