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Optical Coherence Tomography: Imaging Mouse Retinal Ganglion Cells In Vivo
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Deep-learning based multiclass retinal fluid segmentation and detection in optical coherence tomography images using

Donghuan Lu1, Morgan Heisler1, Sieun Lee1

  • 1Simon Fraser University, School of Engineering Science, Burnaby V5A 1S6, Canada.

Medical Image Analysis
|March 12, 2019
PubMed
Summary

A new framework accurately segments and detects retinal fluid in optical coherence tomography (OCT) images. This method won first place in the MICCAI RETOUCH challenge for its high performance in fluid segmentation and detection.

Keywords:
Fully convolutional networkMulticlass segmentation and detectionOptical coherence tomographyRetinal fluid

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computer Vision

Background:

  • Optical coherence tomography (OCT) provides high-resolution 3D retinal imaging.
  • OCT reveals subsurface retinal alterations, including fluid accumulation indicative of vascular disease.
  • Accurate segmentation and detection of retinal fluid are crucial for diagnosing and managing eye conditions.

Purpose of the Study:

  • To propose a novel framework for multiclass fluid segmentation and detection in retinal OCT images.
  • To leverage deep learning and machine learning for enhanced diagnostic capabilities in ophthalmology.

Main Methods:

  • A fully convolutional neural network was trained using OCT image intensity and graph-cut-based retinal layer segmentation.
  • Random forest classification was employed to refine segmented fluid regions and reject false positives.
  • The framework was evaluated in the MICCAI RETOUCH challenge.

Main Results:

  • The proposed framework achieved high performance in fluid segmentation (mean Dice: 0.7667).
  • The system demonstrated excellent performance in fluid detection (mean AUC: 1.00).
  • The framework secured first place in both segmentation and detection tasks at the MICCAI RETOUCH challenge.

Conclusions:

  • The developed framework offers a robust and accurate solution for automated fluid segmentation and detection in OCT scans.
  • This approach has significant potential for improving the diagnosis and monitoring of retinal diseases.
  • The success in the RETOUCH challenge validates the efficacy of the proposed deep learning and machine learning integration.