首页> 外文会议>Image Processing pt.2; Progress in Biomedical Optics and Imaging; vol.6 no.24 >CAD System for Lung Cancer Screening using Low Dose Thick-slice CT Images
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CAD System for Lung Cancer Screening using Low Dose Thick-slice CT Images

机译:使用低剂量厚层CT图像进行肺癌筛查的CAD系统

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Our group developed the computer aided diagnosis (CAD) system for lung cancer in 1996, and has been used in clinical field since 1997. From this CAD system (conventional system), we discovered problem and we attempted to solve the problem by using our proposed algorithm. The proposed algorithm succeeded in the improvement of the following three problems of the conventional system. (1) Weak extraction algorithm of region of interest (ROI) with noise, (2) Poor knowledge of chest structure, and (3) diagnostic processing for nodule of limited size. In this paper, the algorithm that solves problem (2) and (3) is described. We evaluated the proposed algorithm, which was applied to the following four databases. (A) Lung cancer database, (B) detailed examination database, (C) a large-scale screening database by 10mm-thickness images reconstructed from single-slice CT scan, and (D) a large-scale screening database by 10mm-thickness images reconstructed from multi-slice CT scan. The proposed method obtained the following successful results: Lung cancer database 95.7% TP and detailed examination 94.8% TP. For the large-scale screening database, we evaluated each examination process from physicians' reading to cancer decision. The extraction rate of proposed algorithm improved as the examinations proceed. Two false positive results were obtained. False positive 1 (6.8-9.2 shadows / case) needed for a detailed examination and the object of false positive 2 (2.6-4.0 shadows / case) was an abnormal shadow.
机译:我们小组于1996年开发了肺癌计算机辅助诊断(CAD)系统,并于1997年开始在临床领域中使用。从该CAD系统(常规系统)中,我们发现了问题,并尝试通过使用我们提出的解决方案来解决该问题。算法。所提出的算法成功地改善了常规系统的以下三个问题。 (1)带有噪声的感兴趣区域(ROI)的提取算法较弱;(2)胸部结构知识不足;(3)有限大小结节的诊断处理。本文描述了解决问题(2)和(3)的算法。我们评估了所提出的算法,该算法已应用于以下四个数据库。 (A)肺癌数据库,(B)详细检查数据库,(C)通过单层CT扫描重建的10mm厚度图像的大规模筛查数据库,以及(D)通过10mm厚度的大规模筛查数据库多层CT扫描重建的图像。该方法取得了以下成功的结果:肺癌数据库TP为95.7%,详细检查为TP为94.8%。对于大型筛查数据库,我们评估了从医师阅读到癌症决策的每个检查过程。随着检查的进行,提出算法的提取率提高了。获得了两个假阳性结果。详细检查所需的假阳性1(6.8-9.2阴影/例),假阳性2(2.6-4.0阴影/例)的对象是异常阴影。

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