Department of Electrical Engineering, KAIST, Daejeon 305-701, Republic of Korea.
Magn Reson Imaging. 2011 Sep;29(7):1014-22. doi: 10.1016/j.mri.2011.01.005. Epub 2011 May 25.
The objective of this paper was to automatically segment the cerebellum from T1-weighted human brain magnetic resonance (MR) images.
The proposed method constructs a cerebellum template using five sets of 3-T MR imaging (MRI) data, which are used to determine the initial position and the shape prior of the cerebellum for the active contour model. Our formulation includes the active contour model with shape prior, which thereby maintains the shape of the template. The proposed active contour model is sequentially applied to sagittal-, coronal- and transverse-view images. To evaluate the proposed method, it is applied to BrainWeb data and a 3-T MRI data set and compared with FreeSurfer with respect to performance assessment metrics.
The segmented cerebellum was compared with the results from FreeSurfer. Using the manually segmented cerebellum as reference, we measured the average Jaccard coefficients of the proposed method, which were 0.882 and 0.885 for the BrainWeb data and 3-T MRI data set, respectively.
We presented the active contour model with shape prior for extracting the cerebellum from T1-weighted brain MR images. The proposed method yielded a robust and accurate segmentation result.
本文旨在自动分割 T1 加权人脑磁共振（MR）图像中的小脑。
所提出的方法使用五组 3-T MR 成像（MRI）数据构建小脑模板，用于确定主动轮廓模型的小脑初始位置和形状先验。我们的公式包括具有形状先验的主动轮廓模型，从而保持模板的形状。所提出的主动轮廓模型依次应用于矢状面、冠状面和横断面图像。为了评估所提出的方法，将其应用于 BrainWeb 数据和 3-T MRI 数据集，并与 FreeSurfer 进行性能评估指标比较。
将分割的小脑与 FreeSurfer 的结果进行比较。使用手动分割的小脑作为参考，我们测量了所提出方法的平均 Jaccard 系数，对于 BrainWeb 数据和 3-T MRI 数据集分别为 0.882 和 0.885。
我们提出了一种具有形状先验的主动轮廓模型，用于从 T1 加权脑 MR 图像中提取小脑。所提出的方法产生了稳健且准确的分割结果。
