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首页> 外文会议>2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro >Spatial intensity prior correction for tissue segmentation in the developing human brain
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Spatial intensity prior correction for tissue segmentation in the developing human brain

机译：在发展中的人类大脑中进行组织分割之前的空间强度校正

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The degree of white matter (WM) myelination is rather inhomogeneous across the brain. As a consequence, white matter appears differently across the cortical lobes in MR images acquired during early postnatal development. At 1 year old specifically, the gray/white matter contrast of MR images in prefrontal and temporal lobes is limited and thus tissue segmentation results show commonly reduce accuracy in these lobes. In this novel work, we propose the use of spatial intensity growth maps (IGM) for T1 and T2 weighted image to compensate for local appearance inhomogeneity. The IGM captures expected intensity changes from 1 to 2 years of age, as appearance inhomogeneity is highly reduced by the age of 24 months. For that purpose, we employ MRI data from a large dataset of longitudinal (12 and 24 month old subjects) MR study of Autism. The IGM creation is based on automatically co-registered images at 12 months, corresponding registered 24 months images, and a final registration of all image to a prior average template. In template space, voxelwise correspondence is thus achieved and the IGM is computed as the coefficient of a voxelwise linear regression model between corresponding intensities at 1-year and 2-years. The proposed IGM shows low regression values of 1-10% in GM and CSF regions, as well as in WM regions at advanced stage of myelination at 1-year. However, in the prefrontal and temporal lobe we observed regression values of 20-25%, indicating that the IGM appropriately captures the expected large intensity change in these lobes due to myelination.The IGM is applied to cross-sectional MRI datasets of 1-year old subjects via registration, correction and tissue segmentation of the corrected dataset. We validated our approach in a small study of images with known, manual “ground truth” segmentations. We furthermore present an EM-like optimization of adapting existing non-optimal prior atlas probability maps to fit known expert rater segmentations.
机译：大脑中的白质（WM）髓鞘化程度相当不均匀。结果，白质在产​​后早期获得的MR图像的整个皮层叶中出现的方式不同。具体来说，在1岁时，前额叶和颞叶的MR图像的灰/白质对比度受到限制，因此组织分割结果显示通常会降低这些叶的准确性。在这项新颖的工作中，我们建议使用T1和T2加权图像的空间强度增长图（IGM）来补偿局部外观的不均匀性。 IGM捕获了从1到2岁的预期强度变化，因为外观不均匀性在24个月大时已大大降低。为此，我们采用了来自自闭症的纵向（12和24个月大受试者）MR研究的大型数据集的MRI数据。 IGM的创建基于12个月时自动共注册的图像，相应的24个月注册的图像，以及将所有图像最终注册到先前的平均模板中。因此，在模板空间中，实现了体素对应，并且将IGM计算为1年和2年对应强度之间的体素线性回归模型的系数。拟议的IGM在GM和CSF地区以及在1年的髓鞘形成晚期的WM地区显示出1-10％的低回归值。然而，在额叶和颞叶，我们观察到20-25％的回归值，表明IGM适当地捕获了由于髓鞘形成而在这些肺叶中预期的大强度变化.IGM适用于1年的横截面MRI数据集通过对校正后的数据集进行配准，校正和组织分割，可以对老对象进行校正。我们在对带有已知手动“地面真相”分割的图像进行的小型研究中验证了我们的方法。此外，我们还提出了一种类似于EM的优化方法，可以调整现有的非最佳先验图集概率图，以适应已知的专家评分者细分。

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