多项式展开配准的多图谱大脑磁共振图像分割

Segmentation of brain MRI images on multi-atlas for polynomial expansion registration

全脑区的自动分割对于大脑疾病的诊断和治疗具有重要的临床意义。为了提高分割准确率,提出了一种多项式展开配准的多图谱分割算法。利用线性多项式展开模型,结合仿射变换和非刚体变换将待分割的目标图像与图谱图像进行逐一的配准来获取位移场;利用归一化互信息计算二者的相似度,筛选出与目标图像相似度较高的图谱,并对图谱的标记图像进行位移场映射,得到粗分割结果;采用全局加权投票法将粗分割结果进行融合,得到最终的细分割结果。选取了35例来自MICCAI 2012 Multi-Atlas Labeling挑战和10例来自上海交通大学医学院附属第九人民医院的磁共振T1加权图像对该方法进行验证,最终全脑区的DSC值分别为0.7585和0.7351。实验结果表明,算法具有较高的大脑分割精确度和鲁棒性,有望辅助临床进行大脑相关疾病的诊断和治疗。

Segmentation of whole brain regions has important clinical significance for the diagnosis and treatment of brain diseases. In order to improve the accuracy of segmentation, a multi-atlas segmentation algorithm for polynomial expansion registration is proposed. Firstly, by using the model of linear polynomial expansion and combining affine transformation and non-rigid body transformation, the target image to be segmented and the map image are registered one by one to obtain the displacement field. Then, the similarity between the target image and the atlas is calculated by normalized mutual information, in which the atlas with high similarity to the target image is screened out. Then, the marked image of the atlas is mapped by displacement field, and the rough segmentation result is obtained. Finally, the global weighted voting method is used to fuse the obtained coarse segmentation results to obtain the final fine segmentation results. 35 T1-Weighted MRI images from the MICCAI 2012 multi-atlas Labeling challenge and 10 T1-Weighted MRI images from Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine are selected to verify the method. Finally, DSC values of the whole brain region are 0.7585 and 0.7351. Experimental results show that the algorithm has high accuracy and robustness in brain segmentation, which is expected to assist clinical diagnosis and treatment of brain-related diseases.