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Application of Optical Coherence Tomography to a Mouse Model of Retinopathy
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Wavelet scattering transform application in classification of retinal abnormalities using OCT images.

Zahra Baharlouei1, Hossein Rabbani2, Gerlind Plonka3

  • 1Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.

Scientific Reports
|November 4, 2023
PubMed
Summary
This summary is machine-generated.

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This study introduces a Convolutional Neural Network using Wavelet Scattering Transform (WST) for diagnosing retinal abnormalities in Optical Coherence Tomography (OCT) images. The WST-based model achieves high accuracy with reduced computational complexity compared to deep learning methods.

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computer Vision

Background:

  • Computer-Aided Diagnosis (CAD) aids ophthalmologists in detecting retinal abnormalities.
  • Optical Coherence Tomography (OCT) is a key imaging modality for retinal analysis.
  • Deep learning methods often require significant computational resources.

Purpose of the Study:

  • To develop an efficient Convolutional Neural Network (CNN) model for detecting retinal abnormalities using OCT images.
  • To leverage Wavelet Scattering Transform (WST) for improved feature extraction and reduced computational complexity.
  • To evaluate the model's performance on multiple public datasets and compare it with existing state-of-the-art methods.

Main Methods:

  • A two-layer Convolutional Neural Network incorporating Wavelet Scattering Transform (WST) was designed.

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  • WST was employed to generate sparse, translation-invariant, and deformation-stable image representations.
  • Principal Component Analysis (PCA) was utilized for feature classification.
  • The model was validated on four publicly available OCT datasets (OCTID, TOPCON, Heidelberg, Duke).

    • Main Results:

    • The WST-based CNN achieved high classification accuracies on the OCTID dataset (e.g., [Formula: see text] for 5 classes).
    • Accurate detection of Diabetic Macular Edema (DME) was demonstrated on the TOPCON dataset ([Formula: see text]).
    • Strong performance was observed on the Heidelberg ([Formula: see text]) and Duke ([Formula: see text]) datasets, classifying DME, Age-related Macular Degeneration, and Normal cases.
    • The model demonstrated competitive or superior performance compared to state-of-the-art methods with significantly lower computational complexity.

    Conclusions:

    • The proposed WST-based CNN offers an efficient and effective approach for diagnosing retinal abnormalities from OCT images.
    • This method provides a valuable tool for ophthalmologists, potentially improving diagnostic speed and accuracy.
    • The model's reduced computational demands make it a practical solution for clinical applications.