Shi Feng, Yap Pew-Thian, Fan Yong, Cheng Jie-Zhi, Wald Lawrence L, Gerig Guido, Lin Weili, Shen Dinggang
IDEA Lab, Department of Radiology and BRIC, University of North Carolina at Chapel Hill.
Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2009;2009(5204348):39-45. doi: 10.1109/CVPR.2009.5204348.
The acquisition of high quality MR images of neonatal brains is largely hampered by their characteristically small head size and low tissue contrast. As a result, subsequent image processing and analysis, especially for brain tissue segmentation, are often hindered. To overcome this problem, a dedicated phased array neonatal head coil is utilized to improve MR image quality by effectively combing images obtained from 8 coil elements without lengthening data acquisition time. In addition, a subject-specific atlas based tissue segmentation algorithm is specifically developed for the delineation of fine structures in the acquired neonatal brain MR images. The proposed tissue segmentation method first enhances the sheet-like cortical gray matter (GM) structures in neonatal images with a Hessian filter for generation of cortical GM prior. Then, the prior is combined with our neonatal population atlas to form a cortical enhanced hybrid atlas, which we refer to as the subject-specific atlas. Various experiments are conducted to compare the proposed method with manual segmentation results, as well as with additional two population atlas based segmentation methods. Results show that the proposed method is capable of segmenting the neonatal brain with the highest accuracy, compared to other two methods.
新生儿脑部高质量磁共振（MR）图像的采集在很大程度上受到其头部尺寸小且组织对比度低这一特性的阻碍。因此，后续的图像处理与分析，尤其是脑组织分割，常常受到阻碍。为克服这一问题，一种专用的相控阵新生儿头部线圈被用于通过有效合并从8个线圈元件获取的图像来提高MR图像质量，同时不延长数据采集时间。此外，还专门开发了一种基于个体图谱的组织分割算法，用于在采集到的新生儿脑部MR图像中描绘精细结构。所提出的组织分割方法首先使用Hessian滤波器增强新生儿图像中的片状皮质灰质（GM）结构，以生成皮质GM先验。然后，该先验与我们的新生儿群体图谱相结合，形成一个皮质增强混合图谱，我们将其称为个体图谱。进行了各种实验，将所提出的方法与手动分割结果以及另外两种基于群体图谱的分割方法进行比较。结果表明，与其他两种方法相比，所提出的方法能够以最高的准确率分割新生儿脑部。
