Convolutional neural network-based common-path optical coherence tomography A-scan boundary-tracking training and validation using a parallel Monte Carlo synthetic dataset

Shoujing Guo, Jin U. Kang

Research output: Contribution to journalArticlepeer-review

Abstract

We present a parallel Monte Carlo (MC) simulation platform for rapidly generating synthetic common-path optical coherence tomography (CP-OCT) A-scan image dataset for image-guided needle insertion. The computation time of the method has been evaluated on different configurations and 100000 A-scan images are generated based on 50 different eye models. The synthetic dataset is used to train an end-to-end convolutional neural network (Ascan-Net) to localize the Descemet’s membrane (DM) during the needle insertion. The trained Ascan-Net has been tested on the A-scan images collected from the ex-vivo human and porcine cornea as well as simulated data and shows improved tracking accuracy compared to the result by using the Canny-edge detector.

Original languageEnglish (US)
Pages (from-to)25876-25890
Number of pages15
JournalOptics Express
Volume30
Issue number14
DOIs
StatePublished - Jul 4 2022
Externally publishedYes

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics

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