Self-supervised iterative refinement learning for macular OCT volumetric data classification.

Researchers

Journal

Modalities

Models

Abstract

We present self-supervised iterative refinement learning (SIRL) as a pipeline to improve a type of macular optical coherence tomography (OCT) volumetric image classification algorithms. In this type of algorithms, first, two-dimensional (2D) image classification algorithms are applied to each B-scan in an OCT volume, and then B-scan level classification results are combined to obtain the classification result of the volume. Specifically, SIRL consists of repetitive training-sieving-relabeling steps. In the initialization stage, the label of each 2D image is assigned as the label of the volume they belong to, yielding an initial label set. In the training stage, the network is trained using the current label set. In the sieving and relabeling stage, the label of each 2D image is renewed based on the classification result of the trained network, and a new label set is obtained. Experiments are conducted on a clinical dataset and public dataset, on which the performances of the models trained by a normal scheme and our proposed methods are compared under a five-fold cross validation. Our proposed method achieves sensitivity, specificity, and accuracy of 89.74%, 94.87%, and 93.18%, respectively, on the clinical dataset. On the public dataset, the results of the corresponding three metrics are 98.22%, 90.43% and 95.88%. The results demonstrate the effectiveness of our proposed method as an approach to improve the B-scan-classification-based macular OCT volumetric image classification algorithms.
Copyright © 2019 Elsevier Ltd. All rights reserved.