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A Semi-automatic Solitary Pulmonary Nodule Volume Measurement Algorithm on Low-dose CT Images

机译:低剂量CT图像的半自动孤立性肺结节体积测量算法

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The computer-assisted methods for measuring and tracking nodule volumes have the potential to improve precision for indicating of malignancy for indeterminate nodules. In this paper, we propose a semi-automatic geometric solitary pulmonary nodule (SPN) volume measurement algorithm for calculating the precise volume of indeterminate SPNs with low-dose CT (LDCT) images. The algorithm divided the SPN volume into three parts: the SPN core, the parenchymal area, and the partial volume area. Then we calculated the volume with a geometry method and corrected the volume for partial volume effects with the partial volume area. The proposed method has been compared with the manual volume measurement of nodules by radiologists using two sets CT images in vivo. The result shows that the method is more objective and can evaluate the indeterminate nodules growth rate effectively using LDCT images.
机译:用于测量和跟踪结节体积的计算机辅助方法有可能提高指示不确定结节恶性程度的准确性。在本文中,我们提出了一种半自动几何孤立肺结节(SPN)体积测量算法,用于用低剂量CT(LDCT)图像计算不确定的SPN的精确体积。该算法将SPN体积分为三部分:SPN核心,实质区域和部分体积区域。然后,我们使用几何方法计算体积,并通过局部体积区域对局部体积效应的体积进行校正。拟议的方法已经与放射科医生在体内使用两组CT图像与结核的手动体积测量进行了比较。结果表明,该方法更加客观,利用LDCT图像可以有效地评估不确定结节的生长速度。

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