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Optical Coherence Tomography: Imaging Mouse Retinal Ganglion Cells In Vivo

Optical Coherence Tomography: Imaging Mouse Retinal Ganglion Cells In Vivo

Published on: September 22, 2017

Intra-retinal layer segmentation in optical coherence tomography images.

Akshaya Mishra¹, Alexander Wong, Kostadinka Bizheva

¹Dept. of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L3G1, Canada.

|January 7, 2010

Summary

This study presents a computer method for segmenting retinal layers in optical coherence tomography (OCT) images. The novel approach accurately identifies retinal layers despite noise and irregular features, aiding retinal disease diagnosis.

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Area of Science:

Ophthalmology
Medical Imaging
Computer Vision

Background:

Retinal layer thickness in optical coherence tomography (OCT) images is crucial for diagnosing retinal diseases.
Accurate segmentation of retinal layers is challenging due to speckle noise, low contrast, and irregular structures like detachments, holes, and drusen.

Purpose of the Study:

To develop and present a computer-based method for accurate retinal layer segmentation from OCT images.
To overcome limitations of existing methods in handling image noise and complex retinal morphologies.

Main Methods:

An efficient two-step kernel-based optimization scheme was developed.
The method first identifies approximate layer locations and then refines them for precise segmentation.
The algorithm was validated on in-vivo OCT images from healthy and diseased rodent models.

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Main Results:

The proposed method achieved accurate segmentation of retinal layers in OCT images.
The approach demonstrated robustness against speckle noise and low image contrast.
Effective segmentation was achieved even with irregularly shaped structural features present in the images.

Conclusions:

The developed computer method offers a reliable solution for retinal layer segmentation in OCT imaging.
This technique can improve diagnostic accuracy for various retinal diseases by providing precise layer measurements.
The method shows promise for clinical applications, especially in challenging imaging conditions.