Accuracy of bone segmentation and surface generation strategies analyzed by using synthetic CT volumes
Accuracy of bone segmentation and surface generation strategies analyzed by using synthetic CT volumes
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DOI：
10.1111/joa.13383
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发表时间：
2020-12-16
影响因子：
2.4
通讯作者：
Engelkes, Karolin
中科院分区：
文献类型：
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作者：
Engelkes, Karolin
不同种类的骨测量通常来源于计算机断层扫描（CT）体积，以回答生物学和相关领域的众多问题。骨分割的基本步骤和可选的多边形表面生成是保持测量误差小的关键。在本研究中，分析了不同的、易于获取的分割技术（全局阈值分割、自动局部阈值分割、加权随机漫步、神经网络和分水岭分割）和表面生成方法（不同的算法结合不同程度的简化）的性能，并提出了最小化不准确性的建议。不同的方法被应用于合成CT体积，正确的分割和表面几何形状是已知的。通过为灰度直方图设置特定的窗口，然后选择适当的阈值方法和半径，应用自动局部阈值，实现了合成体的最准确分割。Amira (R)模块生成的曲面结合仔细的曲面简化是最准确的。即使对于低对比度-噪声比的合成CT体积，也可以获得亚体素精度的表面。真实CT体积的分割试验支持了这一发现。通过使用易于访问的软件包，可以从CT体积中获得非常准确的分割和表面。提出的结果和推导出的建议将有助于减少未来研究中的测量误差。此外，在未来的研究中，可以采用所展示的分割和表面质量评估策略来量化新分割方法的性能。
Different kinds of bone measurements are commonly derived from computed-tomography (CT) volumes to answer a multitude of questions in biology and related fields. The underlying steps of bone segmentation and, optionally, polygon surface generation are crucial to keep the measurement error small. In this study, the performance of different, easily accessible segmentation techniques (global thresholding, automatic local thresholding, weighted random walk, neural network, and watershed) and surface generation approaches (different algorithms combined with varying degrees of simplification) was analyzed and recommendations for minimizing inaccuracies were derived. The different approaches were applied to synthetic CT volumes for which the correct segmentation and surface geometry were known. The most accurate segmentations of the synthetic volumes were achieved by setting a case-specific window to the gray value histogram and subsequently applying automatic local thresholding with appropriately chosen thresholding method and radius. Surfaces generated by the Amira (R) module Generate Lego Surface in combination with careful surface simplification were the most accurate. Surfaces with sub-voxel accuracy were obtained even for synthetic CT volumes with low contrast-to-noise ratios. Segmentation trials with real CT volumes supported the findings. Very accurate segmentations and surfaces can be derived from CT volumes by using readily accessible software packages. The presented results and derived recommendations will help to reduce the measurement error in future studies. Furthermore, the demonstrated strategies for assessing segmentation and surface qualities can be adopted to quantify the performance of new segmentation approaches in future studies.