Preconditioned intensity-based prostate registration using statistical deformation models

Oliver Zettinig, Julia Rackerseder, Beatrice Lentes, Tobias Maurer, Kay Westenfelder, Matthias Eiber, Benjamin Frisch, Nassir Navab

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Despite the common invisibility of cancerous lesions in transrectal ultrasound (TRUS), TRUS-guided random biopsy is considered the gold standard to diagnose prostate cancer. Pre-interventional magnetic resonance imaging (MRI) has been shown to improve the detection of malignancies but fast and accurate MRI/TRUS registration for multi-modal biopsy guidance remains challenging. In this work, we derive a statistical deformation model (SDM) from 50 automatically segmented patient datasets and propose a novel registration scheme based on a lesion-specific, anisotropic preconditioned similarity metric. The approach is validated on a dataset of 10 patients, showing landmark registration errors of 1.41 mm in the vicinity of suspicious areas.

Original languageEnglish
Title of host publication2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017
PublisherIEEE Computer Society
Pages853-857
Number of pages5
ISBN (Electronic)9781509011711
DOIs
StatePublished - 15 Jun 2017
Event14th IEEE International Symposium on Biomedical Imaging, ISBI 2017 - Melbourne, Australia
Duration: 18 Apr 201721 Apr 2017

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference14th IEEE International Symposium on Biomedical Imaging, ISBI 2017
Country/TerritoryAustralia
CityMelbourne
Period18/04/1721/04/17

Keywords

  • Preconditioning
  • Prostate
  • Registration
  • Statistical Deformation Model

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