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A Decomposable Model for the Detection of Prostate Cancer in Multi-parametric MRI

  • Nathan LayEmail author
  • Yohannes Tsehay
  • Yohan Sumathipala
  • Ruida Cheng
  • Sonia Gaur
  • Clayton Smith
  • Adrian Barbu
  • Le Lu
  • Baris Turkbey
  • Peter L. Choyke
  • Peter Pinto
  • Ronald M. Summers
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)

Abstract

Institutions that specialize in prostate MRI acquire different MR sequences owing to variability in scanning procedure and scanner hardware. We propose a novel prostate cancer detector that can operate in the absence of MR imaging sequences. Our novel prostate cancer detector first trains a forest of random ferns on all MR sequences and then decomposes these random ferns into a sum of MR sequence-specific random ferns enabling predictions to be made in the absence of one or more of these MR sequences. To accomplish this, we first show that a sum of random ferns can be exactly represented by another random fern and then we propose a method to approximately decompose an arbitrary random fern into a sum of random ferns. We show that our decomposed detector can maintain good performance when some MR sequences are omitted.

Notes

Acknowledgements

This research was funded by the Intramural Research Program of the National Institutes of Health, Clinical Center. Data used in this research were obtained from The Cancer Imaging Archive (TCIA) sponsored by the SPIE, NCI/NIH, AAPM, and Radboud University.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Nathan Lay
    • 1
    Email author
  • Yohannes Tsehay
    • 1
  • Yohan Sumathipala
    • 1
  • Ruida Cheng
    • 2
  • Sonia Gaur
    • 3
  • Clayton Smith
    • 3
  • Adrian Barbu
    • 4
  • Le Lu
    • 1
  • Baris Turkbey
    • 3
  • Peter L. Choyke
    • 3
  • Peter Pinto
    • 3
  • Ronald M. Summers
    • 1
  1. 1.Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Clinical Center, National Institutes of HealthBethesdaUSA
  2. 2.Image Science Laboratory, Center of Information Technology, National Institutes of HealthBethesdaUSA
  3. 3.Urologic Oncology Branch and Molecular Imaging Program, National Cancer Institute, National Institutes of HealthBethesdaUSA
  4. 4.Department of StatisticsFlorida State UniversityTallahasseeUSA

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