Image‐ versus histogram‐based considerations in semantic segmentation of pulmonary hyperpolarized gas images

Purpose To characterize the differences between histogram‐based and image‐based algorithms for segmentation of hyperpolarized gas lung images. Methods Four previously published histogram‐based segmentation algorithms (ie, linear binning, hierarchical k‐means, fuzzy spatial c‐means, and a Gaussian mi...

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Vydáno v:Magnetic resonance in medicine Ročník 86; číslo 5; s. 2822 - 2836
Hlavní autoři: Tustison, Nicholas J., Altes, Talissa A., Qing, Kun, He, Mu, Miller, G. Wilson, Avants, Brian B., Shim, Yun M., Gee, James C., Mugler, John P., Mata, Jaime F.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Wiley Subscription Services, Inc 01.11.2021
Témata:
Advanced Normalization Tools
Algorithms
Artificial neural networks
Computer applications
convolutional neural network
Deep learning
Domains
Ecosystem
Fields (mathematics)
functional lung imaging
Histograms
Humans
Image processing
Image Processing, Computer-Assisted
Image segmentation
Lung - diagnostic imaging
Lungs
Machine learning
Magnetic Resonance Imaging
Neural networks
Optimization
Probabilistic models
Retrospective Studies
segmentation
Semantic segmentation
Semantics
Spatial data
Xenon 129
Xenon Isotopes
deep learning
Advanced Normalization Tools
functional lung imaging
segmentation
convolutional neural network
ISSN:0740-3194, 1522-2594, 1522-2594
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Shrnutí:Purpose To characterize the differences between histogram‐based and image‐based algorithms for segmentation of hyperpolarized gas lung images. Methods Four previously published histogram‐based segmentation algorithms (ie, linear binning, hierarchical k‐means, fuzzy spatial c‐means, and a Gaussian mixture model with a Markov random field prior) and an image‐based convolutional neural network were used to segment 2 simulated data sets derived from a public (n = 29 subjects) and a retrospective collection (n = 51 subjects) of hyperpolarized 129Xe gas lung images transformed by common MRI artifacts (noise and nonlinear intensity distortion). The resulting ventilation‐based segmentations were used to assess algorithmic performance and characterize optimization domain differences in terms of measurement bias and precision. Results Although facilitating computational processing and providing discriminating clinically relevant measures of interest, histogram‐based segmentation methods discard important contextual spatial information and are consequently less robust in terms of measurement precision in the presence of common MRI artifacts relative to the image‐based convolutional neural network. Conclusions Direct optimization within the image domain using convolutional neural networks leverages spatial information, which mitigates problematic issues associated with histogram‐based approaches and suggests a preferred future research direction. Further, the entire processing and evaluation framework, including the newly reported deep learning functionality, is available as open source through the well‐known Advanced Normalization Tools ecosystem.
Bibliografie:Funding information
Support for this work includes funding from the National Institutes of Health (NIH) grants, R01HL133889, R01‐CA172595, and S10‐OD018079
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ISSN:0740-3194
1522-2594
1522-2594
DOI:10.1002/mrm.28908