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Deep learning convolutional neural networks for the estimation of liver fibrosis severity from ultrasound texture
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Paper Abstract

Diagnosis and staging of liver fibrosis is a vital prognostic marker in chronic liver diseases. Due to the inaccuracies and risk of complications associated with liver core needle biopsy, the current standard for diagnosis, other less invasive methods are sought for diagnosis. One such method that has been shown to correlate well with liver fibrosis is shear wave velocity measured by ultrasound (US) shear wave elastography; however, this technique requires specific software, hardware, and training. A current perspective in the radiology community is that the texture pattern from an US image may be predictive of the stage of liver fibrosis. We propose the use of convolutional neural networks (CNNs), a framework shown to be well suited for real world image interpretation, to test whether the texture pattern in gray scale elastography images (B-mode US with fixed, subject-agnostic acquisition settings) is predictive of the shear wave velocity (SWV). In this study, gray scale elastography images from over 300 patients including 3,500 images with corresponding SWV measurements were preprocessed and used as input to 100 different CNN architectures that were trained to regress shear wave velocity. In this study, even the best performing CNN explained only negligible variation in the shear wave velocity measures. These extensive test results suggest that the gray scale elastography image texture provides little predictive information about shear wave velocity and liver fibrosis.

Paper Details

Date Published: 13 March 2019
PDF: 8 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109503E (13 March 2019); doi: 10.1117/12.2512592
Show Author Affiliations
Alex Treacher, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
Daniel Beauchamp, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
Bilal Quadri, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
David Fetzer, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
Abhinav Vij, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
Takeshi Yokoo, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
Albert Montillo, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)


Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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