Fast fully automatic brain detection in fetal MRI using dense rotation invariant image descriptors
File(s)
Accepted version
Author(s)
Type
Conference Paper
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.
Date Issued
2014-04-29
Date Acceptance
2014-04-29
Citation
2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), 2014, pp.1230-1233
Publisher
IEEE
Start Page
1230
End Page
1233
Journal / Book Title
2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)
Copyright Statement
© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Source
11th International Symposium on Biomedical Imaging (ISBI)
Notes
keywords: Accuracy;Biomedical imaging;Fetus;Image segmentation;Magnetic resonance imaging;Noise reduction;Three-dimensional displays;fetal MRI reconstruction;fetal brain localization;fetal brain segmentation
Publication Status
Published
Start Date
2014-04-29
Finish Date
2014-05-02
Coverage Spatial
Beijing