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

Alex Treacher, Daniel Beauchamp, Bilal Quadri, David Fetzer, Abhinav Vij, Takeshi Yokoo, Albert Montillo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2019
Subtitle of host publicationComputer-Aided Diagnosis
EditorsKensaku Mori, Horst K. Hahn
PublisherSPIE
ISBN (Electronic)9781510625471
DOIs
StatePublished - Jan 1 2019
EventMedical Imaging 2019: Computer-Aided Diagnosis - San Diego, United States
Duration: Feb 17 2019Feb 20 2019

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10950
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2019: Computer-Aided Diagnosis
CountryUnited States
CitySan Diego
Period2/17/192/20/19

Fingerprint

Elasticity Imaging Techniques
fibrosis
Shear waves
liver
Liver Cirrhosis
Liver
learning
S waves
textures
Textures
Ultrasonics
Learning
Neural networks
gray scale
Large-Core Needle Biopsy
Radiology
Liver Diseases
Chronic Disease
Software
Image texture

Keywords

  • Convolutional Neural Network
  • Deep Learning
  • Liver Fibrosis
  • Random Search
  • Shear Wave Velocity
  • Ultrasound

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

Cite this

Treacher, A., Beauchamp, D., Quadri, B., Fetzer, D., Vij, A., Yokoo, T., & Montillo, A. (2019). Deep learning convolutional neural networks for the estimation of liver fibrosis severity from ultrasound texture. In K. Mori, & H. K. Hahn (Eds.), Medical Imaging 2019: Computer-Aided Diagnosis [109503E] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10950). SPIE. https://doi.org/10.1117/12.2512592

Deep learning convolutional neural networks for the estimation of liver fibrosis severity from ultrasound texture. / Treacher, Alex; Beauchamp, Daniel; Quadri, Bilal; Fetzer, David; Vij, Abhinav; Yokoo, Takeshi; Montillo, Albert.

Medical Imaging 2019: Computer-Aided Diagnosis. ed. / Kensaku Mori; Horst K. Hahn. SPIE, 2019. 109503E (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10950).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Treacher, A, Beauchamp, D, Quadri, B, Fetzer, D, Vij, A, Yokoo, T & Montillo, A 2019, Deep learning convolutional neural networks for the estimation of liver fibrosis severity from ultrasound texture. in K Mori & HK Hahn (eds), Medical Imaging 2019: Computer-Aided Diagnosis., 109503E, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 10950, SPIE, Medical Imaging 2019: Computer-Aided Diagnosis, San Diego, United States, 2/17/19. https://doi.org/10.1117/12.2512592
Treacher A, Beauchamp D, Quadri B, Fetzer D, Vij A, Yokoo T et al. Deep learning convolutional neural networks for the estimation of liver fibrosis severity from ultrasound texture. In Mori K, Hahn HK, editors, Medical Imaging 2019: Computer-Aided Diagnosis. SPIE. 2019. 109503E. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE). https://doi.org/10.1117/12.2512592
Treacher, Alex ; Beauchamp, Daniel ; Quadri, Bilal ; Fetzer, David ; Vij, Abhinav ; Yokoo, Takeshi ; Montillo, Albert. / Deep learning convolutional neural networks for the estimation of liver fibrosis severity from ultrasound texture. Medical Imaging 2019: Computer-Aided Diagnosis. editor / Kensaku Mori ; Horst K. Hahn. SPIE, 2019. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE).
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