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• W4283362293 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." @default.
• W4283362293 created "2022-06-25" @default.
• W4283362293 creator A5043954109 @default.
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• W4283362293 date "2022-07-01" @default.
• W4283362293 modified "2023-10-18" @default.
• W4283362293 title "Convolutional neural network-based common-path optical coherence tomography A-scan boundary-tracking training and validation using a parallel Monte Carlo synthetic dataset" @default.
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• W4283362293 doi "https://doi.org/10.1364/oe.462980" @default.
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