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


Ontology type: schema:Chapter     


Chapter Info

DATE

2018

AUTHORS

Nathan Lay , Yohannes Tsehay , Yohan Sumathipala , Ruida Cheng , Sonia Gaur , Clayton Smith , Adrian Barbu , Le Lu , Baris Turkbey , Peter L. Choyke , Peter Pinto , Ronald M. Summers

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. More... »

PAGES

930-939

References to SciGraph publications

  • 2016. HeMIS: Hetero-Modal Image Segmentation in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2016
  • 2016. Automatic Lymph Node Cluster Segmentation Using Holistically-Nested Neural Networks and Structured Optimization in CT Images in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2016
  • Book

    TITLE

    Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

    ISBN

    978-3-030-00933-5
    978-3-030-00934-2

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-030-00934-2_103

    DOI

    http://dx.doi.org/10.1007/978-3-030-00934-2_103

    DIMENSIONS

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