Biomedical Image Computing Group, Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California 94143, USA.
Hum Brain Mapp. 2010 Sep;31(9):1348-58. doi: 10.1002/hbm.20935.
Imaging of the human fetus using magnetic resonance (MR) is an essential tool for quantitative studies of normal as well as abnormal brain development in utero. However, because of fundamental differences in tissue types, tissue properties and tissue distribution between the fetal and adult brain, automated tissue segmentation techniques developed for adult brain anatomy are unsuitable for this data. In this paper, we describe methodology for automatic atlas-based segmentation of individual tissue types in motion-corrected 3D volumes reconstructed from clinical MR scans of the fetal brain. To generate anatomically correct automatic segmentations, we create a set of accurate manual delineations and build an in utero 3D statistical atlas of tissue distribution incorporating developing gray and white matter as well as transient tissue types such as the germinal matrix. The probabilistic atlas is associated with an unbiased average shape and intensity template for registration of new subject images to the space of the atlas. Quantitative whole brain 3D validation of tissue labeling performed on a set of 14 fetal MR scans (20.57-22.86 weeks gestational age) demonstrates that this atlas-based EM segmentation approach achieves consistently high DSC performance for the main tissue types in the fetal brain. This work indicates that reliable measures of brain development can be automatically derived from clinical MR imaging and opens up possibility of further 3D volumetric and morphometric studies with multiple fetal subjects.
利用磁共振（MR）对人体胎儿进行成像，是对胎儿期正常和异常脑发育进行定量研究的重要工具。然而，由于胎儿和成人脑部之间的组织类型、组织特性和组织分布存在根本差异，为成人脑部解剖结构开发的自动组织分割技术并不适用于这些数据。本文介绍了一种基于图谱的自动分割方法，可对从临床胎儿脑部磁共振扫描重建的运动校正 3D 容积中，对个别组织类型进行分割。为了生成解剖学上正确的自动分割，我们创建了一组准确的手动勾画，并构建了一个包含发育中的灰质和白质以及暂时性组织类型（如生发基质）的胎儿期 3D 统计组织分布图谱。概率图谱与无偏平均形状和强度模板相关联，用于将新的主体图像注册到图谱空间。对一组 14 例胎儿磁共振扫描（20.57-22.86 周胎龄）进行的组织标记全脑 3D 验证表明，这种基于图谱的 EM 分割方法可实现胎儿脑内主要组织类型的一致性高 DSC 性能。这项工作表明，可以从临床磁共振成像中自动推导出可靠的大脑发育测量值，并为具有多个胎儿对象的进一步 3D 体积和形态计量学研究开辟了可能性。
