胰腺的自动分割一直是医学图像分割中一项具有挑战性的问题。胰腺是一个具有高度解剖变异性的器官, 目前的多图谱分割方法很难对胰腺的边缘产生精确的分割。针对这一问题, 采用了基于多图谱配准的分割算法对胰腺进行分割, 优化了一种局部动态阈值的后处理方法。在标签融合阶段, 采用概率阈值融合算法、Majority voting(MV)算法、STAPLE算法和SIMPLE算法四种标签融合算法进行对比。在后处理阶段, 采用局部动态阈值处理方法, 首先通过初步分割结果对目标图像提取目标区域, 然后自动确定阈值实现该区域的二值化, 最终与初步分割结果取交集作为最终分割结果。采用留一交叉验证策略对80例NIH胰腺CT图像和22例来自上海本地医院的胰腺CT图像进行分割, 最终得到的DSC分别为79.98%和81.30%。实验结果表明, 所提方法实现了对胰腺的有效分割。

Automatic segmentation of pancreas has always been a challenging problem in medical image segmentation. The pancreas is an organ with a high degree of anatomical variability, and it is difficult for the current multi-atlas segmentation methods to accurately segment the edges of the pancreas. Focusing on this problem, a segmentation algorithm is adopted based on multi-atlas registration to segment the pancreas, and optimizes a post-processing method of local dynamic threshold. In the label fusion stage, four label fusion algorithms are used for comparison: probability threshold fusion algorithm, Majority voting (MV) algorithm, STAPLE algorithm and SIMPLE algorithm. In the post-processing stage, the local dynamic threshold processing method is adopted. First, the target area is extracted from the target image through the preliminary segmentation result, and then the threshold value is automatically determined to realize the binarization of the area. Finally, the intersection with the preliminary segmentation result is taken as the final segmentation result. A leave-one-out cross-validation strategy was used to segment 80 NIH pancreatic CT images and 22 pancreatic CT images from local hospital at Shanghai, and the final DSC obtained were 79.98% and 81.30%, respectively. The experimental results show that the proposed method achieves effective segmentation of the pancreas.