Heckemann Rolf A, Hajnal Joseph V, Aljabar Paul, Rueckert Daniel, Hammers Alexander
Imaging Sciences Department, MRC Clinical Sciences Centre, Imperial College at Hammersmith Hospital Campus, London, UK.
Med Image Comput Comput Assist Interv. 2006;9(Pt 2):815-22. doi: 10.1007/11866763_100.
Segmentations of MR images of the human brain can be generated by propagating an existing atlas label volume to the target image. By fusing multiple propagated label volumes, the segmentation can be improved. We developed a model that predicts the improvement of labelling accuracy and precision based on the number of segmentations used as input. Using a cross-validation study on brain image data as well as numerical simulations, we verified the model. Fit parameters of this model are potential indicators of the quality of a given label propagation method or the consistency of the input segmentations used.
人脑磁共振图像的分割可以通过将现有的图谱标签体积传播到目标图像来生成。通过融合多个传播的标签体积，可以提高分割效果。我们开发了一个模型，该模型根据用作输入的分割数量来预测标记准确性和精度的提高。通过对脑图像数据进行交叉验证研究以及数值模拟，我们验证了该模型。该模型的拟合参数是给定标签传播方法质量或所用输入分割一致性的潜在指标。
