Primary hepatic malignant tumor is an extremely harmful tumor with high incidence in China. Liver surgery (such as tumor resection, living liver transplantation, etc.) is one of the main treatments for various common benign and malignant liver diseases. The accurate segmentation of liver tissue from medical images is a fundamental and crucial step in computer-assisted liver disease diagnosis and surgical planning. Concerning the specificity and challenge of liver segmentation, an automatic segmentation algorithm model based on three-dimensional convolutional neural network (3DCNN) is proposed. 3DCNN is capable of conducting volume-to-volume learning, which can learn the plane and spatial information of liver images well. Integrating the depth supervision mechanism into 3DCNN can effectively reduce the problem of gradient disappearance or explosion, and speed up the convergence while improving the resolution. Finally, using the initial segmentation result as a priori information, the graph cut algorithm based on multi-convex constraint is used for further segmentation. Experimental results show that the segmentation model can accurately segment liver tissue from abdominal CT images.%原发性肝脏恶性肿瘤是我国高发且危害极大的恶性肿瘤.肝脏手术(如肿瘤切除、活体肝移植等)是各种常见肝脏良恶性疾病的主要治疗方法之一.从医学影像中将肝脏组织准确地分割出来,是计算机辅助肝脏疾病诊断与手术规划中一个基础且至关重要的步骤.针对肝脏分割的特异性及分割难点,提出3D卷积神经网络(3DCNN)肝脏自动分割算法模型.3DCNN基于对体数据的训练能很好地学习到肝脏图像平面与空间信息.通过将深度监督机制无缝地整合到3DCNN中,能够有效解决梯度消失或爆炸的优化问题,加快收敛速度的同时提高分辨能力.最后,将初始分割结果作为先验信息,采用基于多星凸约束的图割算法做进一步的分割优化.实验结果表明该分割模型能够将肝脏组织从腹部CT图像中精确分割.
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