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Filtering by: Creator Cottle, Brian K. Remove constraint Creator: Cottle, Brian K. Creator Hitchcock, Robert W. Remove constraint Creator: Hitchcock, Robert W.

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  • Spectral data for: Towards Intraoperative Quantification of Atrial Fibrosis Using Light-Scattering Spectroscopy and Convolutional Neural Networks

    Description:
    Light-scattering spectroscopy (LSS) is an established optical approach for nondestructive characterization of biological tissues. Here, we investigated the capabilities of LSS and convolutional neural networks (CNNs) to quantitatively characterize the composition and arrangement of cardiac tissues. We assembled tissue constructs from 200 μm thick sections of fixed myocardium and aortic wall. Thickness of the tissue constructs was similar to the thickness of atrial free wall. In the assembled constructs, the aortic sections represented fibrotic tissue and the depth, volume fraction, and arrangement of these fibrotic insets were varied. We gathered spectra with wavelengths from 500-1100 nm from the constructs at multiple locations relative to a light source. We used single and combinations of two spectra for training of CNNs. With independently measured spectra, we assessed the accuracy of the trained CNNs for classification of tissue constructs from single spectra and combined spectra. In general, classification accuracy with single spectra was smaller than with combined spectra. Combined spectra including spectra from fibers distal from the illumination fiber typically yielded a higher accuracy than proximal single collection fibers. Maximal classification accuracy of depth detection, volume fraction and permutated arrangements was (mean±stddev) 88.97±2.49%, 76.33±1.51% and 84.25±1.88%, respectively. Our studies demonstrate the reliability of quantitative characterization of tissue composition and arrangements using a combination of LSS and CNNs. Potential clinical applications of the developed approach include intraoperative quantification and mapping of atrial fibrosis as well as assessment of ablation lesions.
    Subject:
    cardiology
    Creator:
    Hitchcock, Robert W., Sachse, Frank B., Cottle, Brian K., Kelson, Bailey E.B., and Knighton, Nathan J.
    Owner:
    Frank Sachse
    Based Near Label Tesim:
    Salt Lake City, Utah, United States
    Language:
    English
    Date Uploaded:
    01/09/2020
    Date Modified:
    01/22/2022
    Date Created:
    20190101 to 20190208 and 20200721 to 20200807
    Resource Type:
    Dataset
    Identifier:
    https://doi.org/10.7278/S50D-3Q4J-SC4Y

  • Spectral and image data for: Towards Cardiac Tissue Characterization Using Machine Learning and Light-Scattering Spectroscopy

    Description:
    Significance: Current medical imaging systems have many limitations for applications in cardiovascular diseases. New technologies may overcome these limitations. Particularly interesting are technologies for diagnosis of cardiac diseases, e.g. fibrosis, myocarditis, and transplant rejection. Aim: To introduce and assess a new optical system capable of assessing cardiac muscle tissue using light-scattering spectroscopy (LSS) in conjunction with machine learning. Approach: We applied an ovine model to investigate if the new LSS system is capable of estimating densities of cell nuclei in cardiac tissue. We measured the nuclear density using fluorescent labeling, confocal microscopy, and image processing. Spectra acquired from the same cardiac tissues were analyzed with spectral clustering and convolutional neural networks to assess feasibility and reliability of density quantification. Results: Spectral clustering revealed distinct groups of spectra correlated to ranges of nuclear density. Convolutional neural networks correctly classified 3 groups of spectra with low, medium, or high nuclear density with 95.00±11.77% (mean and standard deviation) accuracy. The analysis revealed sensitivity of the accuracy to wavelength range and subsampling of spectra. Conclusions: LSS and machine learning are capable of assessing nuclear density in cardiac tissues. The approach could be useful for diagnosis of cardiac diseases associated with an increase of nuclei.
    Keyword:
    machine learning, diagnosis, neural networks, medical imaging, nuclear density, spectroscopy, spectral imaging, and cardiac
    Subject:
    Cardiology
    Creator:
    Sachse, Frank B., Tiwari, Sarthak, Kaza, Aditya K., Cottle, Brian K., Knighton, Nathan, and Hitchcock, Robert W.
    Owner:
    Brian Cottle
    Based Near Label Tesim:
    Salt Lake City, UT, Utah, United States
    Language:
    English
    Date Uploaded:
    05/11/2021
    Date Modified:
    12/08/2023
    Date Created:
    2019-01-01 to 2019-02-08 and 2020-07-21 to 2020-08-07
    License:
    CC BY NC - Allows others to use and share your data non-commercially and with attribution.
    Resource Type:
    Dataset
    Identifier:
    https://doi.org/10.7278/S50D-FV1Q-BYNS