Automated lung segmentation in digital chest tomosynthesis.
Automated lung segmentation in digital chest tomosynthesis.
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DOI：
10.1118/1.3671939
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发表时间：
2012-02
期刊：
影响因子：
3.8
通讯作者：
Jiahui Wang;J. Dobbins;Qiang Li
中科院分区：
文献类型：
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作者：
Jiahui Wang;J. Dobbins;Qiang Li
目的研究一种用于数字化胸部断层扫描中肺结节的计算机检测的肺自动分割方法。方法作者收集了45张数字断层扫描，并在每次扫描中手动分割参考肺区，以评估该方法的性能。作者通过计算原始图像的边缘梯度来增强肺部轮廓，并将边缘梯度图像转换到极坐标空间，从而实现了该技术的自动化。然后，作者使用动态编程技术在变换的边缘梯度图像中勾勒出未模糊的肺的轮廓。肺部轮廓被转换回原始图像以提供最终的分割结果。由于没有肋骨重叠的肺结构，上述肺分割算法首先应用于中央重建的断层合成切片。然后使用中心切片中的肺分段来指导非中心切片中的肺分割。作者通过使用(1)肺区域重叠率，(2)肺边界的平均绝对距离(MAD)，(3)自动分割的肺与手动分割的参考肺之间的肺边界的Hausdorff距离，以及(4)自动分割的肺中包含的结节的比例来评估该分割方法。结果左肺、右肺和全肺的平均重叠率分别为85.7%、88.3%和87.0%，左肺、右肺和全肺的平均MAD分别为4.8、3.9和4.4 mm，左肺、右肺和全肺的平均Hausdorrf距离分别为25.0 mm、25.5 mm和30.1 mm。用肺分割方法得到的分割肺中，参考肺内的所有结节都被正确地包含在分割的肺中。结论该方法对肺结节的分割具有较高的准确率，可用于数字化断层扫描中肺结节的计算机辅助检测。
PURPOSE The purpose of this study was to develop an automated lung segmentation method for computerized detection of lung nodules in digital chest tomosynthesis. METHODS The authors collected 45 digital tomosynthesis scans and manually segmented reference lung regions in each scan to assess the performance of the method. The authors automated the technique by calculating the edge gradient in an original image for enhancing lung outline and transforming the edge gradient image to polar coordinate space. The authors then employed a dynamic programming technique to delineate outlines of the unobscured lungs in the transformed edge gradient image. The lung outlines were converted back to the original image to provide the final segmentation result. The above lung segmentation algorithm was first applied to the central reconstructed tomosynthesis slice because of the absence of ribs overlapping lung structures. The segmented lung in the central slice was then used to guide lung segmentation in noncentral slices. The authors evaluated the segmentation method by using (1) an overlap rate of lung regions, (2) a mean absolute distance (MAD) of lung borders, (3) a Hausdorff distance of lung borders between the automatically segmented lungs and manually segmented reference lungs, and (4) the fraction of nodules included in the automatically segmented lungs. RESULTS The segmentation method achieved mean overlap rates of 85.7%, 88.3%, and 87.0% for left lungs, right lungs, and entire lungs, respectively; mean MAD of 4.8, 3.9, and 4.4 mm for left lungs, right lungs, and entire lungs, respectively; and mean Hausdorrf distance of 25.0 mm, 25.5 mm, and 30.1 mm for left lungs, right lungs, and entire lungs, respectively. All of the nodules inside the reference lungs were correctly included in the segmented lungs obtained with the lung segmentation method. CONCLUSIONS The method achieved relatively high accuracy for lung segmentation and will be useful for computer-aided detection of lung nodules in digital tomosynthesis.