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Fast fully automatic brain detection in fetal MRI using dense rotation invariant image descriptors

  • Bernhard Kainz
  • , Kevin Keraudren
  • , Vanessa Kyriakopoulou
  • , Mary Rutherford
  • , Joseph V. Hajnal
  • , Daniel Rueckert

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

32 Scopus citations

Abstract

Automatic detection of the fetal brain in Magnetic Resonance (MR) Images is especially difficult due to arbitrary orientation of the fetus and possible movements during the scan. In this paper, we propose a method to facilitate fully automatic brain voxel classification by means of rotation invariant volume descriptors. We calculate features for a set of 50 prenatal fast spin echo T2 volumes of the uterus and learn the appearance of the fetal brain in the feature space. We evaluate our novel classification method and show that we can localize the fetal brain with an accuracy of 100% and classify fetal brain voxels with an accuracy above 97%. Furthermore, we show how the classification process can be used for a direct segmentation of the brain by simple refinement methods within the raw MR scan data leading to a final segmentation with a Dice score above 0.90.

Original languageEnglish
Title of host publication2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1230-1233
Number of pages4
ISBN (Electronic)9781467319591
DOIs
StatePublished - 29 Jul 2014
Externally publishedYes
Event11th IEEE International Symposium on Biomedical Imaging, ISBI 2014 - Beijing, China
Duration: 29 Apr 20142 May 2014

Publication series

Name2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014

Conference

Conference11th IEEE International Symposium on Biomedical Imaging, ISBI 2014
Country/TerritoryChina
CityBeijing
Period29/04/142/05/14

Keywords

  • Fetal MRI reconstruction
  • Fetal brain localization
  • Fetal brain segmentation

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