Automatic brain localization in fetal MRI using superpixel graphs

Alansary A, Lee M, Keraudren K, Kainz B, Malamateniou C, Rutherford M, Hajnal JV, Glocker B, Rueckert D (2015)

Publication Type: Conference contribution

Publication year: 2015

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Verlag

Book Volume: 9487

Pages Range: 13-22

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Lille, FRA

ISBN: 9783319279282

DOI: 10.1007/978-3-319-27929-9_2

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.

Authors with CRIS profile

Bernhard Kainz

Involved external institutions

King’s College London GB United Kingdom (GB) Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB)

How to cite

APA:

Alansary, A., Lee, M., Keraudren, K., Kainz, B., Malamateniou, C., Rutherford, M.,... Rueckert, D. (2015). Automatic brain localization in fetal MRI using superpixel graphs. In Kanwal K. Bhatia, Herve Lombaert (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 13-22). Lille, FRA: Springer Verlag.

MLA:

Alansary, Amir, et al. "Automatic brain localization in fetal MRI using superpixel graphs." Proceedings of the 1st International Workshop on Machine Learning Meets Medical Imaging, MLMMI 2015, Lille, FRA Ed. Kanwal K. Bhatia, Herve Lombaert, Springer Verlag, 2015. 13-22.

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