An iterative multi-atlas patch-based approach for cortex segmentation from neonatal MRI

Abstract : Brain structure analysis in the newborn is a major health issue. This is especially the case for premature neonates, in order to obtain predictive information related to the child development. In particular, the cortex is a structure of interest, that can be observed in MRI (magnetic resonance imaging). However, neonatal MRI data present specific properties that make them challenging to process. In this context, multi-atlas approaches constitute an efficient strategy, taking advantage of images processed beforehand. The method proposed in this article relies on such multi-atlas strategy. More precisely, it uses two paradigms: first, a non-local model based on patches; second, an iterative optimization scheme. Coupling both concepts allows us to consider patches related not only to the image information, but also to the current segmentation. This strategy is compared to other multi-atlas methods proposed in the literature. Experiments show that the proposed approach provides robust cortex segmentation results.
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https://hal.univ-reims.fr/hal-01761063
Contributor : Nicolas Passat <>
Submitted on : Saturday, April 7, 2018 - 4:14:47 PM
Last modification on : Tuesday, July 23, 2019 - 1:40:03 PM

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Carlos Tor Díez, Nicolas Passat, Isabelle Bloch, Sylvain Faisan, Nathalie Bednarek, et al.. An iterative multi-atlas patch-based approach for cortex segmentation from neonatal MRI. Computerized Medical Imaging and Graphics, Elsevier, In press, ⟨10.1016/j.compmedimag.2018.09.003⟩. ⟨hal-01761063v1⟩

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