Shape "break-and-repair" strategy and its application to automated medical image segmentation.
Shape "break-and-repair" strategy and its application to automated medical image segmentation.
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DOI：
10.1109/tvcg.2010.56
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发表时间：
2011-01
影响因子：
5.2
通讯作者：
Rubin GD
中科院分区：
文献类型：
--
作者：
Pu J;Paik DS;Meng X;Roos JE;Rubin GD
在三维医学成像中，特定解剖结构的分割通常是计算机辅助检测/诊断（CAD）目的的预处理步骤，其性能对疾病诊断以及治疗效果的客观定量评估具有重大影响。然而，各种疾病的存在、图像噪声或伪影以及个体解剖结构的差异通常对特定结构的精确分割构成挑战。为了解决这些问题，本研究提出了一种称为“断裂与修复”的形状分析策略，以促进医学图像的自动分割。类似于使用有限数量的控制点进行表面逼近，其基本思想是去除有问题的区域，然后通过将剩余区域高精度地表示为隐函数来估计一个平滑且完整的表面形状。这种形状分析策略的创新之处在于能够在一个统一的框架内解决具有挑战性的医学图像分割问题，而不论所涉及的解剖结构如何变化。在我们的实现中，主曲率分析用于识别和去除有问题的区域，基于径向基函数（RBF）的隐式曲面拟合用于实现闭合（或完整）的表面边界。通过将该策略应用于CT检查所描绘的两种完全不同的解剖结构（即人肺和肺结节）的自动分割，证明了该策略的可行性和性能。我们对从不同来源收集的大量临床CT检查进行的定量实验证明了形状“断裂与修复”策略在医学图像分割中的准确性、稳健性和通用性。
In three-dimensional medical imaging, segmentation of specific anatomy structure is often a preprocessing step for computer-aided detection/diagnosis (CAD) purposes, and its performance has a significant impact on diagnosis of diseases as well as objective quantitative assessment of therapeutic efficacy. However, the existence of various diseases, image noise or artifacts, and individual anatomical variety generally impose a challenge for accurate segmentation of specific structures. To address these problems, a shape analysis strategy termed “break-and-repair” is presented in this study to facilitate automated medical image segmentation. Similar to surface approximation using a limited number of control points, the basic idea is to remove problematic regions and then estimate a smooth and complete surface shape by representing the remaining regions with high fidelity as an implicit function. The innovation of this shape analysis strategy is the capability of solving challenging medical image segmentation problems in a unified framework, regardless of the variability of anatomical structures in question. In our implementation, principal curvature analysis is used to identify and remove the problematic regions and radial basis function (RBF) based implicit surface fitting is used to achieve a closed (or complete) surface boundary. The feasibility and performance of this strategy are demonstrated by applying it to automated segmentation of two completely different anatomical structures depicted on CT examinations, namely human lungs and pulmonary nodules. Our quantitative experiments on a large number of clinical CT examinations collected from different sources demonstrate the accuracy, robustness, and generality of the shape “break-and-repair” strategy in medical image segmentation.