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Evaluation of cortical segmentation pipelines on clinical neonatal MRI data

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Abstract

Magnetic Resonance Imaging (MRI) can provide 3D morphological information on brain structures. Such information is particularly relevant for carrying out morphometric brain analysis, especially in the newborn and in the case of prematurity. However, 3D neonatal MRI acquired in clinical environments are low-resolution, anisotropic images, making segmentation a challenging task. In this context, preprocessing techniques aim to increase the image resolution. Interpolation techniques were classically used; super-resolution (SR) techniques have recently appeared as an emerging alternative. In this paper, we evaluate the performance of different SR methods against the classical interpolation in the application of neonatal cortex segmentation. Additionally, we assess the robustness of different segmentation methods for each estimation of high resolution MRI input. Results are evaluated both qualitatively and quantitatively with neonatal clinical MRI.
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Dates and versions

hal-01896545 , version 1 (23-04-2019)

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Carlos Tor-Díez, Chi-Hieu Pham, Hélène Meunier, Sylvain Faisan, Isabelle Bloch, et al.. Evaluation of cortical segmentation pipelines on clinical neonatal MRI data. Engineering in Medicine and Biology Conference (EMBC), 2019, Berlin, Germany. pp.6553-6556, ⟨10.1109/EMBC.2019.8856795⟩. ⟨hal-01896545⟩
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