Computer-aided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy.
Computer-aided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy.
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DOI：
10.1186/s12938-015-0120-7
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发表时间：
2016-01-06
影响因子：
3.9
通讯作者：
Valentim R
中科院分区：
文献类型：
--
作者：
Firmino M;Angelo G;Morais H;Dantas MR;Valentim R
近几十年来，用于肺癌检测和诊断的CADE和CADx系统一直是重要的研究领域。然而，这些领域正在单独开展工作。CADE系统不能显示肿瘤的放射学特征，而CADx系统不能发现结节，自动化程度也不高。因此，这些系统还没有广泛应用于临床环境。本文的目的是开发一种新的CT图像上肺结节的检测和诊断系统，将它们组合成一个单一的系统来识别和表征结节，以提高自动化水平。本文还分别介绍了利用分水岭和定向梯度直方图(HOG)技术区分可能的结节和肺结节的特征提取。对于诊断，它是基于恶性肿瘤的可能性，从而允许放射科医生做出更多辅助决策。使用了基于规则的分类器和支持向量机来消除误报。本研究使用的数据库包括随机从LIDC-IDRI获得的420例病例。分割方法的准确率为97%，检测系统的灵敏度为94.4%，每例假阳性7.04例。发现不同类型的结节(孤立结节、胸膜旁结节、血管旁结节和磨玻璃结节)，直径在3毫米到30毫米之间。对于恶性肿瘤的诊断，我们的系统提供了ROC曲线，区域为：极不可能恶性的结节0.91，中等不可能恶性的结节0.80，不确定恶性的结节0.72，中度怀疑恶性的结节0.67，高度怀疑恶性的结节0.83。从我们的初步结果来看，我们相信我们的系统在临床应用上是很有前途的，可以帮助放射科医生检测和诊断肺癌。
CADe and CADx systems for the detection and diagnosis of lung cancer have been important areas of research in recent decades. However, these areas are being worked on separately. CADe systems do not present the radiological characteristics of tumors, and CADx systems do not detect nodules and do not have good levels of automation. As a result, these systems are not yet widely used in clinical settings. The purpose of this article is to develop a new system for detection and diagnosis of pulmonary nodules on CT images, grouping them into a single system for the identification and characterization of the nodules to improve the level of automation. The article also presents as contributions: the use of Watershed and Histogram of oriented Gradients (HOG) techniques for distinguishing the possible nodules from other structures and feature extraction for pulmonary nodules, respectively. For the diagnosis, it is based on the likelihood of malignancy allowing more aid in the decision making by the radiologists. A rule-based classifier and Support Vector Machine (SVM) have been used to eliminate false positives. The database used in this research consisted of 420 cases obtained randomly from LIDC-IDRI. The segmentation method achieved an accuracy of 97 % and the detection system showed a sensitivity of 94.4 % with 7.04 false positives per case. Different types of nodules (isolated, juxtapleural, juxtavascular and ground-glass) with diameters between 3 mm and 30 mm have been detected. For the diagnosis of malignancy our system presented ROC curves with areas of: 0.91 for nodules highly unlikely of being malignant, 0.80 for nodules moderately unlikely of being malignant, 0.72 for nodules with indeterminate malignancy, 0.67 for nodules moderately suspicious of being malignant and 0.83 for nodules highly suspicious of being malignant. From our preliminary results, we believe that our system is promising for clinical applications assisting radiologists in the detection and diagnosis of lung cancer.
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