Fast fully automatic segmentation of the human placenta from motion corrupted MRI

Alansary A, Kamnitsas K, Davidson A, Khlebnikov R, Rajchl M, Malamateniou C, Rutherford M, Hajnal JV, Glocker B, Rueckert D, Kainz B (2016)


Publication Type: Conference contribution

Publication year: 2016

Journal

Publisher: Springer Verlag

Book Volume: 9901 LNCS

Pages Range: 589-597

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

ISBN: 9783319467221

DOI: 10.1007/978-3-319-46723-8_68

Abstract

Recently,magnetic resonance imaging has revealed to be important for the evaluation of placenta’s health during pregnancy. Quantitative assessment of the placenta requires a segmentation,which proves to be challenging because of the high variability of its position,orientation,shape and appearance. Moreover,image acquisition is corrupted by motion artifacts from both fetal and maternal movements. In this paper we propose a fully automatic segmentation framework of the placenta from structural T2-weighted scans of the whole uterus,as well as an extension in order to provide an intuitive pre-natal view into this vital organ. We adopt a 3D multi-scale convolutional neural network to automatically identify placental candidate pixels. The resulting classification is subsequently refined by a 3D dense conditional random field,so that a high resolution placental volume can be reconstructed from multiple overlapping stacks of slices. Our segmentation framework has been tested on 66 subjects at gestational ages 20–38 weeks achieving a Dice score of 71.95 ± 19.79% for healthy fetuses with a fixed scan sequence and 66.89 ± 15.35% for a cohort mixed with cases of intrauterine fetal growth restriction using varying scan parameters.

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How to cite

APA:

Alansary, A., Kamnitsas, K., Davidson, A., Khlebnikov, R., Rajchl, M., Malamateniou, C.,... Kainz, B. (2016). Fast fully automatic segmentation of the human placenta from motion corrupted MRI. In Gozde Unal, Sebastian Ourselin, Leo Joskowicz, Mert R. Sabuncu, William Wells (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 589-597). Springer Verlag.

MLA:

Alansary, Amir, et al. "Fast fully automatic segmentation of the human placenta from motion corrupted MRI." Proceedings of the Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Ed. Gozde Unal, Sebastian Ourselin, Leo Joskowicz, Mert R. Sabuncu, William Wells, Springer Verlag, 2016. 589-597.

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