A column-based deep learning method for the detection and quantification of atrophy associated with AMD in OCT scans

Adi Szeskin, Roei Yehuda, Or Shmueli, Jaime Levy, Leo Joskowicz*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

The objective quantification of retinal atrophy associated with age-related macular degeneration (AMD) is required for clinical diagnosis, follow-up, treatment efficacy evaluation, and clinical research. Spectral Domain Optical Coherence Tomography (OCT) has become an essential imaging technology to evaluate the macula. This paper describes a novel automatic method for the identification and quantification of atrophy associated with AMD in OCT scans and its visualization in the corresponding infrared imaging (IR) image. The method is based on the classification of light scattering patterns in vertical pixel-wide columns (A-scans) in OCT slices (B-scans) in which atrophy appears with a custom column-based convolutional neural network (CNN). The network classifies individual columns with 3D column patches formed by adjacent neighboring columns from the volumetric OCT scan. Subsequent atrophy columns form atrophy segments which are then projected onto the IR image and are used to identify and segment atrophy lesions in the IR image and to measure their areas and distances from the fovea. Experimental results on 106 clinical OCT scans (5,207 slices) in which cRORA atrophy (the end point of advanced dry AMD) was identified in 2,952 atrophy segments and 1,046 atrophy lesions yield a mean F1 score of 0.78 (std 0.06) and an AUC of 0.937, both close to the observer variability. Automated computer-based detection and quantification of atrophy associated with AMD using a column-based CNN classification in OCT scans can be performed at expert level and may be a useful clinical decision support and research tool for the diagnosis, follow-up and treatment of retinal degenerations and dystrophies.

Original languageAmerican English
Article number102130
JournalMedical Image Analysis
Volume72
DOIs
StatePublished - Aug 2021

Bibliographical note

Funding Information:
We thank Israel Weiss, an undergraduate student at the School of Computer Science and Engineering, and Alona Peretz, a medical student at the Hadassah University Medical Center for their meaningful contribution to the initial stages of this research.

Publisher Copyright:
© 2021 Elsevier B.V.

Keywords

  • CNN deep learning
  • Column-based OCT scattering
  • OCT scan analysis
  • Retinal atrophy in dry age-related macular degeneration

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