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Comparative analysis of alignment algorithms for macular optical coherence tomography imaging.

Craig K Jones1,2, Bochong Li3, Jo-Hsuan Wu4

  • 1Wilmer Eye Institute, School of Medicine, Johns Hopkins University, 600 N. Wolfe Street, Baltimore, MD, 21287, USA.

International Journal of Retina and Vitreous
|October 2, 2023
PubMed
Summary

Aligning Optical coherence tomography (OCT) B-scans significantly improves 3D convolutional neural network (CNN) performance for detecting age-related macular degeneration (AMD). The Advanced Normalization Tools Symmetric image Normalization (ANTs-SyN) algorithm demonstrated robust B-scan alignment, enhancing diagnostic accuracy.

Keywords:
Age-related macular degenerationB-scansImage alignmentImage registrationOptical coherence tomography

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Machine Learning

Background:

  • Optical coherence tomography (OCT) is vital for diagnosing macular diseases in ophthalmology.
  • OCT data is typically a series of 2D B-scans, limiting 3D analysis for machine learning.
  • Aligning B-scans into a 3D volume is crucial for maximizing OCT's machine learning potential.

Purpose of the Study:

  • To evaluate five B-scan registration algorithms for OCT volumes.
  • To introduce a novel metric for quantifying B-scan alignment quality.
  • To demonstrate the impact of B-scan alignment on 3D CNN performance for AMD detection.

Main Methods:

  • Five registration algorithms were tested on OCT B-scans from 48 AMD patients and 50 controls.
  • Registration quality was assessed using an en face surface map and its Laplace difference.
  • A 3D CNN was trained to detect AMD using both aligned and unaligned OCT B-scans.

Main Results:

  • B-scan alignment significantly improved surface map smoothness (mean Laplace difference reduced from ~27 to ~4-5 pixels).
  • The Advanced Normalization Tools Symmetric image Normalization (ANTs-SyN) algorithm showed robust performance.
  • The 3D CNN achieved higher AMD detection accuracy with aligned scans (AUC 0.95 vs. 0.89).

Conclusions:

  • A novel metric effectively quantifies OCT B-scan alignment.
  • Publicly available algorithms significantly improve OCT B-scan alignment, with ANTs-SyN being most effective.
  • Aligned OCT B-scans enhance the performance of 3D CNN models for AMD detection.