20 March 2015 Automatic brain extraction in fetal MRI using multi-atlas-based segmentation
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Abstract
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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Sébastien Tourbier, Sébastien Tourbier, Patric Hagmann, Patric Hagmann, Maud Cagneaux, Maud Cagneaux, Laurent Guibaud, Laurent Guibaud, Subrahmanyam Gorthi, Subrahmanyam Gorthi, Marie Schaer, Marie Schaer, Jean-Philippe Thiran, Jean-Philippe Thiran, Reto Meuli, Reto Meuli, Meritxell Bach Cuadra, Meritxell Bach Cuadra, } "Automatic brain extraction in fetal MRI using multi-atlas-based segmentation", Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 94130Y (20 March 2015); doi: 10.1117/12.2081777; https://doi.org/10.1117/12.2081777
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