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Utility of multispectral imaging for nuclear classification of routine clinical histopathology imagery

Journal Article · · BMC Cell Biology
 [1];  [2];  [3];  [2];  [4]
  1. Univ. of California, Santa Barbara, CA (United States). Electrical and Computer Engineering Dept.; Los Alamos National Lab. (LANL), Los Alamos, NM (United States); DOE/OSTI
  2. Univ. of California, Santa Barbara, CA (United States). Electrical and Computer Engineering Dept.
  3. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  4. Yale Univ., New Haven, CT (United States). Dept. of Pathology

Background: We present an analysis of the utility of multispectral versus standard RGB imagery for routine H&E stained histopathology images, in particular for pixel-level classification of nuclei. Our multispectral imagery has 29 spectral bands, spaced 10 nm within the visual range of 420–700 nm. It has been hypothesized that the additional spectral bands contain further information useful for classification as compared to the 3 standard bands of RGB imagery. We present analyses of our data designed to test this hypothesis. Results: For classification using all available image bands, we find the best performance (equal tradeoff between detection rate and false alarm rate) is obtained from either the multispectral or our "ccd" RGB imagery, with an overall increase in performance of 0.79% compared to the next best performing image type. For classification using single image bands, the single best multispectral band (in the red portion of the spectrum) gave a performance increase of 0.57%, compared to performance of the single best RGB band (red). Additionally, red bands had the highest coefficients/ preference in our classifiers. Principal components analysis of the multispectral imagery indicates only two significant image bands, which is not surprising given the presence of two stains. Conclusion: Our results indicate that multispectral imagery for routine H&E stained histopathology provides minimal additional spectral information for a pixel-level nuclear classification task than would standard RGB imagery.

Research Organization:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Organization:
USDOE Office of Science (SC); National Science Foundation (NSF)
OSTI ID:
1626368
Journal Information:
BMC Cell Biology, Journal Name: BMC Cell Biology Journal Issue: S1 Vol. 8; ISSN 1471-2121
Publisher:
BioMed CentralCopyright Statement
Country of Publication:
United States
Language:
English

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Cited By (14)

Application of multispectral imaging in quantitative immunohistochemistry study of breast cancer: a comparative study journal November 2015
Segmenting Diabetic Retinopathy Lesions in Multispectral Images Using Low-Dimensional Spatial-Spectral Matrix Representation journal February 2020
Histopathological Image Analysis: A Review journal January 2009
Development of Multiscale Biological Image Data Analysis: Review of 2006 International Workshop on Multiscale Biological Imaging, Data Mining and Informatics, Santa Barbara, USA (BII06) journal July 2007
Segmentation of epidermal tissue with histopathological damage in images of haematoxylin and eosin stained human skin journal February 2014
Computational Pathology: Challenges and Promises for Tissue Analysis text January 2016
Improving Prostate Cancer Classification: A Round Robin Forward Sequential Selection Approach
  • Bouatmane, Sabrina; Bouridane, Ahmed; Ali, Mohamed
  • Prostate Cancer - Original Scientific Reports and Case Studies https://doi.org/10.5772/26142
book November 2011
Photothermal multispectral image cytometry for quantitative histology of nanoparticles and micrometastasis in intact, stained and selectively burned tissues journal October 2010
In-vivo multispectral video endoscopy towards in-vivo hyperspectral video endoscopy journal July 2016
Hyperspectral imaging for tissue classification, a way toward smart laparoscopic colorectal surgery journal January 2019
Classification of hyperspectral images for detection of hepatic carcinoma cells based on spectral–spatial features of nucleus journal December 2019
Detection of pancreatic tumor cell nuclei via a hyperspectral analysis of pathological slides based on stain spectra journal January 2019
Using spectral imaging for the analysis of abnormalities for colorectal cancer: When is it helpful? journal June 2018
Artificial Intelligence in Lung Cancer Pathology Image Analysis journal October 2019

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