STAPLE大脑皮层表面沟回分割算法

Cortical Surface Parcellation Using STAPLE Algorithm

提出根据多种大脑皮层表面沟回分割结果预测潜在的概率最优分割,同时自动预测各分割算法性能参数的方法。概率最优分割被建模为多个分割决策的加权组合,利用最大期望算法,以估计的性能参数为依据,迭代地求取权重的最优解。然后利用隐马尔可夫模型,在预测的概率最优分割中引入空间一致性限制条件,将预测的最优分割优化为具有空间一致性的分割决策结果。仿真数据及根据3种典型大脑皮层表面分割算法得到的结果,证明了该算法能有效提高大脑沟回分割的精度,同时自动衡量以后算法的性能指标。

An algorithm for simultaneous truth and performance estimation of various approaches for human cortical surface parcellation was proposed.The probabilistic true segmentation was estimated as a weighted combination of the segmentations resulted from multiple methods.Afterward,an Expectation-Maximization algorithm was used to optimize the weighting depending on the estimated performance level of each method.Furthermore,a spatial homogeneity constraint modeled by the Hidden Markov Random Field theory was incorporated to refine the estimated true segmentation into a spatially homogenous decision.The proposed method was evaluated using both synthetic and real data.Also,it was used to generate reference sulci regions to perform a comparison study of three methods for cortical surface parcellation.The experimental results demonstrate the validity of the proposed method.