Toward automated segmentation of the pathological lung in CT
Toward automated segmentation of the pathological lung in CT
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DOI：
10.1109/tmi.2005.851757
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发表时间：
2005-08-01
影响因子：
10.6
通讯作者：
van Ginneken, B
中科院分区：
文献类型：
--
作者：
Sluimer, I;Prokop, M;van Ginneken, B
肺部区域分割的传统方法依赖于肺野与周围组织之间较大的灰度值对比度。这些方法在肺部存在密集病变的扫描图像上会失效，而此类扫描在临床实践中经常出现。我们提出一种基于配准的分割方案，即将正常肺部的扫描图像弹性配准到包含病变的扫描图像上。当将得到的变换应用于正常肺部的掩模时，就能得到病变肺部的分割结果。作为正常肺部的掩模，我们使用由15次配准的正常扫描图像的分割结果构建的概率分割。为了细化分割，对变换后的概率掩模边界周围的一定体积进行体素分类。将该方案的性能与其他三种算法进行比较：一种传统算法、一种用户交互算法和一种体素分类算法。这些算法在10个包含高密度病变的三维薄切片计算机断层扫描图像上进行测试。通过在体积重叠和边界定位测量方面与手动分割结果进行比较，对得到的分割结果进行评估。基于阈值技术的传统方法和用户交互方法无法对病变进行分割，并且在性能上不如体素分类和改进的基于配准的分割方法。改进的配准方案还有一个额外的优点，即它不需要病变（手动分割的）训练数据。
Conventional methods of lung segmentation rely on a large gray value contrast between lung fields and surrounding tissues. These methods fail on scans with lungs that contain dense pathologies, and such scans occur frequently in clinical practice. We propose a segmentation-by-registration scheme in which a scan with normal lungs is elastically registered to a scan containing pathology. When the resulting transformation is applied to a mask of the normal lungs, a segmentation is found for the pathological lungs. As a mask of the normal lungs, a probabilistic segmentation built up out of the segmentations of 15 registered normal scans is used. To refine the segmentation, voxel classification is applied to a certain volume around the borders of the transformed probabilistic mask. Performance of this scheme is compared to that of three other algorithms: a conventional, a user-interactive and a voxel classification method. The algorithms are tested on 10 three-dimensional thin-slice computed tomography volumes containing high-density pathology. The resulting segmentations are evaluated by comparing them to manual segmentations in terms of volumetric overlap and border positioning measures. The conventional and user-interactive methods that start off with thresholding techniques fail to segment the pathologies and are outperformed by both voxel classification and the refined segmentation-by-registration. The refined registration scheme enjoys the additional benefit that it does not require pathological (hand-segmented) training data.