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28 January 2023 Signal to noise ratio quantifies the contribution of spectral channels to classification of human head and neck tissues ex vivo using deep learning and multispectral imaging
George S. Liu, Jared A. Shenson, Joyce E. Farrell, Nikolas H. Blevins
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Journal of Biomedical Optics, Vol. 28, Issue 1, 016004 (January 2023). https://doi.org/10.1117/1.JBO.28.1.016004
Abstract

Significance

Accurate identification of tissues is critical for performing safe surgery. Combining multispectral imaging (MSI) with deep learning is a promising approach to increasing tissue discrimination and classification. Evaluating the contributions of spectral channels to tissue discrimination is important for improving MSI systems.

Aim

Develop a metric to quantify the contributions of individual spectral channels to tissue classification in MSI.

Approach

MSI was integrated into a digital operating microscope with three sensors and seven illuminants. Two convolutional neural network (CNN) models were trained to classify 11 head and neck tissue types using white light (RGB) or MSI images. The signal to noise ratio (SNR) of spectral channels was compared with the impact of channels on tissue classification performance as determined using CNN visualization methods.

Results

Overall tissue classification accuracy was higher with use of MSI images compared with RGB images, both for classification of all 11 tissue types and binary classification of nerve and parotid (p < 0.001). Removing spectral channels with SNR > 20 reduced tissue classification accuracy.

Conclusions

The spectral channel SNR is a useful metric for both understanding CNN tissue classification and quantifying the contributions of different spectral channels in an MSI system.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
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George S. Liu, Jared A. Shenson, Joyce E. Farrell, and Nikolas H. Blevins "Signal to noise ratio quantifies the contribution of spectral channels to classification of human head and neck tissues ex vivo using deep learning and multispectral imaging," Journal of Biomedical Optics 28(1), 016004 (28 January 2023). https://doi.org/10.1117/1.JBO.28.1.016004
Received: 15 September 2022; Accepted: 6 January 2023; Published: 28 January 2023
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Cited by 4 scholarly publications.
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KEYWORDS
Nervous system
Tissues
RGB color model
Signal to noise ratio
Image classification
Education and training
Nerve
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