Scheenstra Alize E H, van de Ven Rob C G, van der Weerd Louise, van den Maagdenberg Arn M J M, Dijkstra Jouke, Reiber Johan H C
Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands.
Mol Imaging. 2009 Jan-Feb;8(1):35-44.
Segmentation of magnetic resonance imaging (MRI) data is required for many applications, such as the comparison of different structures or time points, and for annotation purposes. Currently, the gold standard for automated image segmentation is nonlinear atlas-based segmentation. However, these methods are either not sufficient or highly time consuming for mouse brains, owing to the low signal to noise ratio and low contrast between structures compared with other applications. We present a novel generic approach to reduce processing time for segmentation of various structures of mouse brains, in vivo and ex vivo. The segmentation consists of a rough affine registration to a template followed by a clustering approach to refine the rough segmentation near the edges. Compared with manual segmentations, the presented segmentation method has an average kappa index of 0.7 for 7 of 12 structures in in vivo MRI and 11 of 12 structures in ex vivo MRI. Furthermore, we found that these results were equal to the performance of a nonlinear segmentation method, but with the advantage of being 8 times faster. The presented automatic segmentation method is quick and intuitive and can be used for image registration, volume quantification of structures, and annotation.
磁共振成像（MRI）数据的分割在许多应用中都是必需的，比如不同结构或时间点的比较以及用于注释目的。目前，自动图像分割的金标准是基于非线性图谱的分割。然而，由于与其他应用相比，小鼠大脑的信噪比低且结构之间的对比度低，这些方法对于小鼠大脑来说要么不够充分，要么非常耗时。我们提出了一种新颖的通用方法来减少对小鼠大脑体内和体外各种结构进行分割的处理时间。该分割包括对模板进行粗略的仿射配准，然后采用聚类方法在边缘附近细化粗略分割。与手动分割相比，所提出的分割方法在体内MRI的12个结构中的7个以及体外MRI的12个结构中的11个上，平均kappa指数为0.7。此外，我们发现这些结果与非线性分割方法的性能相当，但优势在于速度快8倍。所提出的自动分割方法快速且直观，可用于图像配准、结构的体积量化和注释。
