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Multiple surface segmentation using convolution neural nets: application to retinal layer segmentation in OCT images.

Abhay Shah¹, Leixin Zhou¹, Michael D Abrámoff^1,2,3

¹Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, USA.

Biomedical Optics Express

|January 8, 2019

Summary

This summary is machine-generated.

A new convolutional neural network (CNN) framework accurately segments multiple retinal surfaces in optical coherence tomography (OCT) images, improving diagnosis for conditions like age-related macular degeneration (AMD). This deep learning approach offers faster and more precise results than traditional methods.

Keywords:

(100.2960) Image analysis (100.4996) Pattern recognition, neural networks (110.4500) Optical coherence tomography (170.1610) Clinical applications (170.4470) Ophthalmology

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Area of Science:

Biomedical image analysis
Medical imaging
Deep learning in ophthalmology

Background:

Accurate segmentation of retinal surfaces in optical coherence tomography (OCT) is essential for diagnosing and managing retinal diseases.
Traditional graph-based methods require complex, application-specific parameters and manual tuning.
Deep learning offers a data-driven approach to learn features and models directly from training data.

Purpose of the Study:

To develop and validate a novel convolutional neural network (CNN) framework for simultaneous segmentation of multiple retinal surfaces.
To assess the performance of the proposed CNN framework on OCT images of normal retinas and those affected by intermediate age-related macular degeneration (AMD).
To compare the accuracy and computational efficiency of the CNN framework against existing state-of-the-art segmentation methods.

Main Methods:

A single CNN was trained to segment three retinal surfaces in OCT images.
The framework was applied to B-scans from normal and intermediate AMD retinas.
Validation was performed on 50 retinal OCT volumes (3000 B-scans).

Main Results:

The proposed CNN framework achieved statistically significant improvements in segmentation accuracy compared to optimal surface segmentation with convex priors (OSCS) and UNET-based methods.
The method demonstrated high performance on both normal and intermediate AMD retinal OCT data.
The average computation time for segmenting an entire OCT volume was 12.3 seconds, indicating low computational cost.

Conclusions:

The developed CNN framework provides an accurate, efficient, and automated solution for multi-surface retinal segmentation in OCT images.
This deep learning approach holds significant potential for improving quantitative image analysis in ophthalmology, aiding in disease diagnosis and management.
The method's speed and accuracy surpass existing techniques, offering a promising tool for clinical applications.