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Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

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Published on: March 3, 2023

Automatic choroidal segmentation in OCT images using supervised deep learning methods.

Jason Kugelman¹, David Alonso-Caneiro^2,3, Scott A Read¹

¹Contact Lens and Visual Optics Laboratory, School of Optometry and Vision Science, Queensland University of Technology, Brisbane, Queensland, Australia.

Scientific Reports

|September 19, 2019

Summary

This summary is machine-generated.

Deep learning methods accurately segment choroidal tissue boundaries in optical coherence tomography (OCT) images, addressing limitations of manual analysis for ocular disease research.

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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Area of Science:

Ophthalmology
Medical Imaging
Computational Biology

Background:

Choroidal analysis is vital for understanding ocular diseases and physiology.
Optical coherence tomography (OCT) provides detailed choroidal imaging.
Current OCT instruments lack automated choroidal segmentation, making manual analysis time-consuming and impractical.

Purpose of the Study:

To develop and evaluate deep learning methods for accurate automatic segmentation of choroidal tissue boundaries in OCT images.
To compare the performance of different deep learning architectures, patch sizes, and contrast enhancement techniques.
To address the need for efficient and reliable choroidal boundary segmentation in OCT imaging.

Main Methods:

Implementation of various patch-based and fully-convolutional deep learning models.
Systematic testing of network architectures, patch sizes, and contrast enhancement strategies.
Comparison of automated segmentation results against manual annotations (ground-truth) and standard image analysis techniques.

Main Results:

Deep learning methods demonstrated high accuracy in segmenting choroidal boundaries.
Optimal network architectures and parameters were identified for improved performance.
Performance was validated against manual segmentation and traditional methods, showing significant benefits.

Conclusions:

Deep learning offers a reliable and efficient solution for automated choroidal boundary segmentation in OCT images.
This approach significantly improves the analysis of the chorio-retinal boundary, aiding in ocular disease research.
The developed methods overcome the limitations of manual segmentation, enabling large-scale image analysis.