Fast, Sequence Adaptive Parcellation of Brain MR Using Parametric Models

Oula Puonti, Juan Eugenio Iglesias, Koen Van Leemput

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

In this paper we propose a method for whole brain parcellation using the type of generative parametric models typically used in tissue classification. Compared to the non-parametric, multi-atlas segmentation techniques that have become popular in recent years, our method obtains state-of-the-art segmentation performance in both cortical and subcortical structures, while retaining all the benefits of generative parametric models, including high computational speed, automatic adaptiveness to changes in image contrast when different scanner platforms and pulse sequences are used, and the ability to handle multi-contrast (vector-valued intensities) MR data. We have validated our method by comparing its segmentations to manual delineations both within and across scanner platforms and pulse sequences, and show preliminary results on multi-contrast test-retest scans, demonstrating the feasibility of the approach.
Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention – MICCAI 2013 : 16th International Conference, Nagoya, Japan, September 22-26, 2013, Proceedings, Part I
PublisherSpringer
Publication date2013
Pages727-734
ISBN (Print)978-3-642-40810-6
ISBN (Electronic)978-3-642-40811-3
DOIs
Publication statusPublished - 2013
Event16th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2013) - Nagoya, Japan
Duration: 22 Sep 201326 Sep 2013
http://www.miccai2013.org/

Conference

Conference16th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2013)
Country/TerritoryJapan
CityNagoya
Period22/09/201326/09/2013
Internet address
SeriesLecture Notes in Computer Science
Volume8149
ISSN0302-9743

Keywords

  • Image segmentation
  • Scanning
  • Models

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