Multi-phase simultaneous segmentation of tumor in lung 4D-CT data with context information.
Multi-phase simultaneous segmentation of tumor in lung 4D-CT data with context information.
复制标题
具有上下文信息的肺部 4D-CT 数据中肿瘤的多相同步分割
DOI：
10.1371/journal.pone.0178411
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Zhang Y
中科院分区：
文献类型：
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作者：
Shen Z;Wang H;Xi W;Deng X;Chen J;Zhang Y
肺部4D计算机断层扫描（4D-CT）在高精度放射治疗中起着重要作用，因为它表征了呼吸运动，这对于准确的靶定义至关重要。然而，由于肺部4D-CT数据切片数量庞大，因此对肺部肿瘤进行手动分割对于医生来说是一项繁重的工作。同时，肿瘤分割仍然是计算机辅助诊断中的一个众所周知的挑战性问题。本文提出了一种新的基于改进的图割算法的上下文信息约束的肺部4D-CT肿瘤分割方法。我们将肺部4D-CT的所有相位组合成全局图，并相应地构造全局能量函数。首先为每个阶段构造子图。通过在相邻阶段之间添加上下文约束，在每个阶段中强制执行上下文代价项以实现分割结果。最后通过组合所有代价项构造全局能量函数。通过解决最大流量/最小切割问题来实现优化，这导致在所有肺部4D-CT相位中对肿瘤进行同时和鲁棒的分割。通过对10例不同肺部4D-CT病例的实验，验证了该方法的有效性。通过与无上下文约束的图割、水平集方法和具有星星形状先验的图割的比较，表明该方法具有更高的分割精度和鲁棒性。
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.