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Morphology-driven automatic segmentation of MR images of the neonatal brain

机译：形态学驱动的新生儿脑MR图像自动分割

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摘要

The segmentation of MR images of the neonatal brain is an essential step in the study and evaluation of infant brain development. State-of-the-art methods for adult brain MRI segmentation are not applicable to the neonatal brain, due to large differences in structure and tissue properties between newborn and adult brains. Existing newborn brain MRI segmentation methods either rely on manual interaction or require the use of atlases or templates, which unavoidably introduces a bias of the results towards the population that was used to derive the atlases. We propose a different approach for the segmentation of neonatal brain MRI, based on the infusion of high-level brain morphology knowledge, regarding relative tissue location, connectivity and structure. Our method does not require manual interaction, or the use of an atlas, and the generality of its priors makes it applicable to different neonatal populations, while avoiding atlas-related bias. The proposed algorithm segments the brain both globally (intracranial cavity, cerebellum, brainstem and the two hemispheres) and at tissue level (cortical and subcortical gray matter, myelinated and unmyelinated white matter, and cerebrospinal fluid). We validate our algorithm through visual inspection by medical experts, as well as by quantitative comparisons that demonstrate good agreement with expert manual segmentations. The algorithm's robustness is verified by testing on variable quality images acquired on different machines, and on subjects with variable anatomy (enlarged ventricles, preterm- vs. term-born).
机译：新生儿脑部MR图像的分割是研究和评估婴儿脑部发育的必不可少的步骤。成人脑MRI分割的最新方法不适用于新生儿脑，这是由于新生脑和成人脑在结构和组织特性上的巨大差异。现有的新生儿脑部MRI分割方法要么依靠手动交互，要么需要使用地图集或模板，这不可避免地导致结果偏向于用于导出地图集的人群。基于对高级脑形态学知识的灌输，有关相对组织位置，连通性和结构，我们提出了一种不同的新生儿脑MRI分割方法。我们的方法不需要手动交互或使用图集，并且其先验的普遍性使其可适用于不同的新生儿人群，同时避免了与图集相关的偏见。所提出的算法将大脑整体（颅内腔，小脑，脑干和两个半球）和组织水平（皮质和皮质下灰质，有髓和无髓白质以及脑脊液）分割。我们通过医学专家的目视检查以及通​​过定量比较证明我们的算法得到了验证，这些比较表明与专家手工分割的一致性很高。通过在不同机器上获取的可变质量图像以及具有可变解剖结构（心室增大，早产与足月出生）的受试者进行测试，验证了算法的鲁棒性。

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