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Automatic brain extraction in fetal MRI using multi-atlas-based segmentation

Type of publication Peer-reviewed
Publikationsform Proceedings (peer-reviewed)
Author Tourbier Sébastien, Hagmann Patric, Cagneaux Maud, Guibaud Laurent, Gorthi Subrahmanyam, Schaer Marie, Thiran Jean Philippe, Meuli Reto, Bach Cuadra Meritxell,
Project Novel Image Processing Methods for Fetal MR Imaging: 3D Reconstruction and Segmentation with Soft Priors
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Proceedings (peer-reviewed)

Title of proceedings Proc. SPIE 9413, Medical Imaging 2015: Image Processing
Place Orlando, Florida, United States
DOI 10.1117/12.2081777

Abstract

© 2015 SPIE. In fetal brain MRI, most of the high-resolution reconstruction algorithms rely on brain segmentation as a preprocessing step. Manual brain segmentation is however highly time-consuming and therefore not a realistic solution. In this work, we assess on a large dataset the performance of Multiple Atlas Fusion (MAF) strategies to automatically address this problem. Firstly, we show that MAF significantly increase the accuracy of brain segmentation as regards single-atlas strategy. Secondly, we show that MAF compares favorably with the most recent approach (Dice above 0.90). Finally, we show that MAF could in turn provide an enhancement in terms of reconstruction quality.
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