Shen Zhengwen, Wang Huafeng, Xi Weiwen, Deng Xiaogang, Chen Jin, Zhang Yu
School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China.
Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China.
PLoS One. 2017 Jun 16;12(6):e0178411. doi: 10.1371/journal.pone.0178411. eCollection 2017.
Lung 4D computed tomography (4D-CT) plays an important role in high-precision radiotherapy because it characterizes respiratory motion, which is crucial for accurate target definition. However, the manual segmentation of a lung tumor is a heavy workload for doctors because of the large number of lung 4D-CT data slices. Meanwhile, tumor segmentation is still a notoriously challenging problem in computer-aided diagnosis. In this paper, we propose a new method based on an improved graph cut algorithm with context information constraint to find a convenient and robust approach of lung 4D-CT tumor segmentation. We combine all phases of the lung 4D-CT into a global graph, and construct a global energy function accordingly. The sub-graph is first constructed for each phase. A context cost term is enforced to achieve segmentation results in every phase by adding a context constraint between neighboring phases. A global energy function is finally constructed by combining all cost terms. The optimization is achieved by solving a max-flow/min-cut problem, which leads to simultaneous and robust segmentation of the tumor in all the lung 4D-CT phases. The effectiveness of our approach is validated through experiments on 10 different lung 4D-CT cases. The comparison with the graph cut without context constraint, the level set method and the graph cut with star shape prior demonstrates that the proposed method obtains more accurate and robust segmentation results.
肺部四维计算机断层扫描（4D-CT）在高精度放射治疗中发挥着重要作用，因为它能够表征呼吸运动，而呼吸运动对于准确界定靶区至关重要。然而，由于肺部4D-CT数据切片数量众多，医生手动分割肺部肿瘤的工作量很大。同时，肿瘤分割在计算机辅助诊断中仍然是一个极具挑战性的难题。在本文中，我们提出了一种基于改进的带上下文信息约束的图割算法的新方法，以找到一种便捷且稳健的肺部4D-CT肿瘤分割方法。我们将肺部4D-CT的所有相位合并为一个全局图，并据此构建一个全局能量函数。首先为每个相位构建子图。通过在相邻相位之间添加上下文约束来施加一个上下文代价项，以在每个相位实现分割结果。最后通过组合所有代价项构建一个全局能量函数。通过求解最大流/最小割问题来实现优化，这导致在所有肺部4D-CT相位中同时且稳健地分割肿瘤。我们通过对10个不同的肺部4D-CT病例进行实验，验证了我们方法的有效性。与无上下文约束的图割、水平集方法以及带星形先验的图割进行比较表明，所提出的方法获得了更准确和稳健的分割结果。
