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Automatic brain localization in fetal MRI using superpixel graphs

Research output: Chapter in Book/Report/Conference proceedingConference paper

Amir Alansary, Matthew Lee, Kevin Keraudren, Bernhard Kainz, Christina Malamateniou, Mary Rutherford, J V. Hajnal, Ben Glocker, Daniel Rueckert

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer-Verlag Berlin Heidelberg
Pages13-22
Number of pages10
Volume9487
ISBN (Print)9783319279282
DOIs
Publication statusPublished - 30 Dec 2015
Event1st International Workshop on Machine Learning Meets Medical Imaging, MLMMI 2015 - Lille, France
Duration: 11 Jul 201511 Jul 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9487
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference1st International Workshop on Machine Learning Meets Medical Imaging, MLMMI 2015
CountryFrance
CityLille
Period11/07/201511/07/2015

King's Authors

Abstract

Fetal MRI is emerging as an effective, non-invasive tool in prenatal diagnosis and pregnancy follow-up. However, there is a significant variability of the position and orientation of the fetus in the MR images. This makes these images more difficult to analyze and interpret compared to standard adult MR imaging, which standardized anatomical imaging aligned planes. We address this issue by automatic localization of the fetal anatomy, in particular, the brain which is a structure of interest for many fetal MRI studies. We first extract superpixels followed by the computation of a histogram of features for each superpixel using bag of words based on dense scale invariant feature transform (DSIFT) descriptors. We construct a graph of superpixels and train a random forest classifier to distinguish between brain and non-brain superpixels. The localization framework has been tested on 55 MR datasets at gestational ages between 20–38 weeks. The proposed method was evaluated using 5-fold cross validation achieving a 94.55% brain detection accuracy rate.

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