Deep learning applied to hyperspectral endoscopy for online spectral classification.

Scientific Reports
Alexandru GrigoroiuSarah E Bohndiek

Abstract

Hyperspectral imaging (HSI) is being explored in endoscopy as a tool to extract biochemical information that may improve contrast for early cancer detection in the gastrointestinal tract. Motion artefacts during medical endoscopy have traditionally limited HSI application, however, recent developments in the field have led to real-time HSI deployments. Unfortunately, traditional HSI analysis methods remain unable to rapidly process the volume of hyperspectral data in order to provide real-time feedback to the operator. Here, a convolutional neural network (CNN) is proposed to enable online classification of data obtained during HSI endoscopy. A five-layered CNN was trained and fine-tuned on a dataset of 300 hyperspectral endoscopy images acquired from a planar Macbeth ColorChecker chart and was able to distinguish between its 18 constituent colors with an average accuracy of 94.3% achieved at 8.8 fps. Performance was then tested on a set of images simulating an endoscopy environment, consisting of color charts warped inside a rigid tube mimicking a lumen. The algorithm proved robust to such variations, with classification accuracies over 90% being obtained despite the variations, with an average drop in accuracy of 2.4% being r...Continue Reading

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Citations

Jun 20, 2020·Journal of Biophotonics·Asgeir BjorganLise L Randeberg
Sep 12, 2020·Molecular Oncology·Yubo TangRebecca Richards-Kortum
Oct 6, 2020·Biomedical Optics Express·Asgeir Bjorgan, Lise Lyngsnes Randeberg
Mar 7, 2021·Journal of Biophotonics·YuChen XiangPeter Török
May 21, 2021·Lasers in Surgery and Medicine·Lei TianNicholas J Durr
Oct 6, 2021·Scientific Reports·Raquel LeonGustavo M Callico

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Methods Mentioned

BETA
biopsies
biopsy
tissue

Software Mentioned

HySE
GeForce
Theano

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