A novel multi-atlas based image segmentation method is proposed by integrating a semi-supervised label propagation method and a supervised random forests method in a pattern recognition based label fusion framework. The semi-supervised label propagation method takes into consideration local and global image appearance of images to be segmented and segments the images by propagating reliable segmentation results obtained by the supervised random forests method. Particularly, the random forests method is used to train a regression model based on image patches of atlas images for each voxel of the images to be segmented. The regression model is used to obtain reliable segmentation results to guide the label propagation for the segmentation. The proposed method has been compared with state-of-the-art multi-atlas based image segmentation methods for segmenting the hippocampus in MR images. The experiment results have demonstrated that our method obtained superior segmentation performance.

翻译：通过将半监督标签传播方法和受监督随机森林方法纳入基于图案识别的标签聚合框架,提出了基于图案识别标签传播方法和受监督随机森林方法的新多地图图解分化方法。半监督标签传播方法考虑到要分割的图象的当地和全球图像外观和图象部分,通过传播通过受监督随机森林方法获得的可靠的分化结果,宣传可靠的分化结果。特别是,随机森林方法用来对每个要分割的图象的反转图象图谱进行回归模型培训。 回归模型用于获取可靠的分化结果,以指导分化的标签传播。拟议方法与MR图象中用于分化河马峰的状态式多图谱图像分化方法进行了比较。实验结果表明,我们的方法获得了超高级分化性功能。