TY - JOUR
T1 - Unsupervised super-resolution reconstruction of hyperspectral histology images for whole-slide imaging
AU - Ma, Ling
AU - Rathgeb, Armand
AU - Mubarak, Hasan
AU - Tran, Minh
AU - Fei, Baowei
N1 - Publisher Copyright:
© 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.
PY - 2022/5/1
Y1 - 2022/5/1
N2 - Significance: Hyperspectral imaging (HSI) provides rich spectral information for improved histopathological cancer detection. However, acquiring high-resolution HSI data for whole-slide imaging (WSI) can be time-consuming and requires a huge amount of storage space. Aim: WSI using a color camera can be achieved with fast speed, high image resolution, and excellent image quality due to the established techniques. We aim to develop an RGB-guided unsupervised hyperspectral super-resolution reconstruction method that is hypothesized to improve image quality while maintaining the spectral characteristics. Approach: High-resolution hyperspectral images of 32 histologic slides were obtained via automated WSI. High-resolution RGB histology images were registered to the hyperspectral images for RGB guidance. An unsupervised super-resolution network was trained to take the downsampled low-resolution hyperspectral patches (LR-HSI) and high-resolution RGB patches (HR-RGB) as inputs to reconstruct high-resolution hyperspectral patches (HR-HSI). Then, an Inception-based network was trained with the HR-RGB, original HR-HSI, and generated HR-HSI, respectively, for whole-slide histopathological cancer detection. Results: Our super-resolution reconstruction network generated high-resolution hyperspectral images with well-maintained spectral characteristics and improved image quality. Image classification using the original hyperspectral data outperformed RGB because of the extra spectral information. The generated hyperspectral image patches further improved the results. Conclusions: The proposed method potentially reduces image acquisition time, saves storage space without compromising image quality, and improves the image classification performance.
AB - Significance: Hyperspectral imaging (HSI) provides rich spectral information for improved histopathological cancer detection. However, acquiring high-resolution HSI data for whole-slide imaging (WSI) can be time-consuming and requires a huge amount of storage space. Aim: WSI using a color camera can be achieved with fast speed, high image resolution, and excellent image quality due to the established techniques. We aim to develop an RGB-guided unsupervised hyperspectral super-resolution reconstruction method that is hypothesized to improve image quality while maintaining the spectral characteristics. Approach: High-resolution hyperspectral images of 32 histologic slides were obtained via automated WSI. High-resolution RGB histology images were registered to the hyperspectral images for RGB guidance. An unsupervised super-resolution network was trained to take the downsampled low-resolution hyperspectral patches (LR-HSI) and high-resolution RGB patches (HR-RGB) as inputs to reconstruct high-resolution hyperspectral patches (HR-HSI). Then, an Inception-based network was trained with the HR-RGB, original HR-HSI, and generated HR-HSI, respectively, for whole-slide histopathological cancer detection. Results: Our super-resolution reconstruction network generated high-resolution hyperspectral images with well-maintained spectral characteristics and improved image quality. Image classification using the original hyperspectral data outperformed RGB because of the extra spectral information. The generated hyperspectral image patches further improved the results. Conclusions: The proposed method potentially reduces image acquisition time, saves storage space without compromising image quality, and improves the image classification performance.
KW - RGB guidance
KW - hyperspectral imaging
KW - image classification
KW - super-resolution reconstruction
KW - unsupervised
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U2 - 10.1117/1.JBO.27.5.056502
DO - 10.1117/1.JBO.27.5.056502
M3 - Article
C2 - 35578386
AN - SCOPUS:85130205757
SN - 1083-3668
VL - 27
JO - Journal of biomedical optics
JF - Journal of biomedical optics
IS - 5
M1 - 056502
ER -