Segmentation and Image Analysis of Abnormal Lungs at CT: Current Approaches, Challenges, and Future Trends
Segmentation and Image Analysis of Abnormal Lungs at CT: Current Approaches, Challenges, and Future Trends
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DOI：
10.1148/rg.2015140232
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发表时间：
2015-07-01
期刊：
影响因子：
5.5
通讯作者：
Mollura, Daniel J.
中科院分区：
文献类型：
--
作者：
Mansoor, Awais;Bagci, Ulas;Mollura, Daniel J.
基于计算机的计算机断层扫描 (CT) 图像识别肺部与周围胸部组织边界的过程（称为分割）是放射学肺部图像分析中至关重要的第一步。许多算法和软件平台提供了用于量化肺部异常的图像分割例程；然而，几乎所有当前的图像分割方法只有在肺部表现出最小的病理状况或没有病理状况的情况下才适用。当肺部存在中度至大量具有挑战性形状或外观的疾病或异常时，计算机辅助检测系统很可能由于分割方法不准确而无法描绘这些异常区域。特别是胸腔积液、实变和肿块等异常常常导致肺部分割不准确，这极大地限制了图像处理方法在临床和研究中的使用。在这篇综述中，对当前 CT 图像肺部分割方法进行了重要总结，特别强调了该方法在异常情况和具有典型病理结果的情况下的准确性和性能。当前可用的分割方法可分为五类：（a）基于阈值的方法、（b）基于区域的方法、（c）基于形状的方法、（d）邻近解剖引导的方法和（e）基于机器学习的方法。通过 CT 图像上观察到的最常见的肺部异常来解释和说明每一类的可行性及其缺点。概述中，详细介绍了结合所提供的放射科医生方法的实际应用和不断发展的技术。 (C) 北美放射学会，2015
The computer-based process of identifying the boundaries of lung from surrounding thoracic tissue on computed tomographic (CT) images, which is called segmentation, is a vital first step in radiologic pulmonary image analysis. Many algorithms and software platforms provide image segmentation routines for quantification of lung abnormalities; however, nearly all of the current image segmentation approaches apply well only if the lungs exhibit minimal or no pathologic conditions. When moderate to high amounts of disease or abnormalities with a challenging shape or appearance exist in the lungs, computer-aided detection systems may be highly likely to fail to depict those abnormal regions because of inaccurate segmentation methods. In particular, abnormalities such as pleural effusions, consolidations, and masses often cause inaccurate lung segmentation, which greatly limits the use of image processing methods in clinical and research contexts. In this review, a critical summary of the current methods for lung segmentation on CT images is provided, with special emphasis on the accuracy and performance of the methods in cases with abnormalities and cases with exemplary pathologic findings. The currently available segmentation methods can be divided into five major classes: (a) thresholding-based, (b) region-based, (c) shapebased, (d) neighboring anatomy-guided, and (e) machine learning-based methods. The feasibility of each class and its shortcomings are explained and illustrated with the most common lung abnormalities observed on CT images. In an overview, practical applications and evolving technologies combining the presented approaches for the practicing radiologist are detailed. (C) RSNA, 2015