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Liver Tissue Classification in Patients with Hepatocellular Carcinoma by Fusing Structured and Rotationally Invariant Context Representation

  • John TreilhardEmail author
  • Susanne Smolka
  • Lawrence Staib
  • Julius Chapiro
  • MingDe Lin
  • Georgy Shakirin
  • James S. Duncan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10435)

Abstract

This work addresses multi-class liver tissue classification from multi-parameter MRI in patients with hepatocellular carcinoma (HCC), and is among the first to do so. We propose a structured prediction framework to simultaneously classify parenchyma, blood vessels, viable tumor tissue, and necrosis, which overcomes limitations related to classifying these tissue classes individually and consecutively. A novel classification framework is introduced, based on the integration of multi-scale shape and appearance features to initiate the classification, which is iteratively refined by augmenting the feature space with both structured and rotationally invariant label context features. We study further the topic of rotationally invariant label context feature representations, and introduce a method for this purpose based on computing the energies of the spherical harmonic decompositions computed at different frequencies and radii. We test our method on full 3D multi-parameter MRI volumes from 47 patients with HCC and achieve promising results.

Keywords

Classification Structured prediction Rotationally invariant context features Spherical harmonics HCC MRI 

Notes

Acknowledgements

This research was supported in part by NIH grant R01CA206180.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • John Treilhard
    • 1
    Email author
  • Susanne Smolka
    • 3
    • 4
  • Lawrence Staib
    • 1
    • 2
    • 3
  • Julius Chapiro
    • 3
  • MingDe Lin
    • 3
    • 5
  • Georgy Shakirin
    • 6
  • James S. Duncan
    • 1
    • 2
    • 3
  1. 1.Department of Biomedical EngineeringYale UniversityNew HavenUSA
  2. 2.Department of Electrical EngineeringYale UniversityNew HavenUSA
  3. 3.Department of Radiology and Biomedical ImagingYale UniversityNew HavenUSA
  4. 4.Charité University HospitalBerlinGermany
  5. 5.Philips Research North AmericaCambridgeUSA
  6. 6.Philips Research AachenAachenGermany

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