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摘要: 肺部CT图像病变区域检测是肺病辅助诊断技术的重要研究内容，其通过自动分析CT图像并输出病变区域的位置和尺寸等信息，帮助放射科医生做出决策，有利于肺病的早期发现与治疗.本文回顾了肺部CT图像中病变区域自动检测方法所取得的进步，并引入一个通用框架表示和描述现有方法，对2012年以来肺部病变区域辅助检测算法进行了系统性分析和性能汇总.最后讨论了目前存在的问题和有待克服的困难，探讨了未来可能的发展方向.Abstract: Automatic detection of lesion regions in lung CT images is an important research topic in computer aided diagnosis of lung diseases. The system can automatically analyze CT images, output the locations and sizes of lesion regions to help radiologists make decisions, and promote early detection and therapy of lung diseases. In this paper we review the achieved progress of automatic detection methods of lesion regions in lung CT image, and introduce a generic structure for expressing and describing existing detection methods. Furthermore, we provide a systematic analysis and comprehensive performance summary of the latest detection algorithms from 2012. Finally, we point out the challenges ahead, and discuss the future direction of computer aided detection of lung lesions.
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Key words:
- Lung CT /
- lung nodule /
- lung vessel /
- lymph node /
- computer aided detection
1) 本文责任编委 张道强 -
表 1 检测算法评价指标
Table 1 The evaluation index of detection method
指标名称 简要描述 使用场景 敏感度 即真阳性率, 表示被正确检测出的阳性比率 检测和分类评测 特异度 即真阴性率, 表示被正确识别为阴性的比率 分类评测 假阳性数 每个扫描数据集的检测结果中的假阳性个数, PFs/scan 检测和分类评测 假阴性数 每个扫描数据集的检测结果中的假阴性个数, FNs/scan 分类评测 $A_{z}$ 受试者操作特性曲线(ROC)下的面积 分类评测 Dice系数 一种集合相似度度量函数, 公式为: $s = \frac{2\left| X \bigcap Y \right |}{\left | X\right| + \left | Y\right |}$ 分割评测 Hausdorff距离[105] 一种基于对象边缘的、两对象间几何距离的度量方法 检测和分割评测 MAD距离[106] 两对象间的平均绝对距离, 同样基于对象边缘进行计算 检测和分割评测 
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表 2 肺部病变区域检测方法的性能比较
Table 2 The performance comparison of detection methods for lung lesion region
作者 年份 数据集 敏感度(%) 特异度(%) 假阳性 $A_z$ DSC 响应时间 研究对象 算法类型 张永强等[12] 2012 私有良性:75slices
恶性: 47slices- - - - 0.88
0.834.25s
4.36s结节分割 形态结构 王青竹等[92] 2013 私有scans: 150
Slices: 8250; LR: 109897.05 - 9.21 0.995 - - 结节检测 统计模
式识别Carvalho等[13] 2014 公开LIDC-IDRI; 800scans 85.91 97.70 1.82FP/scan
0.008FP/slice0.8062 - 13.56min/scan 结节检测 混合方法 Camarlinghi等[6] 2012 公开LIDC; scans: 69;
nodules: 11485 - 25FP/exam - - - 结节检测 混合方法 高婷等[7] 2014 私有scans:66; ST:2mm;
LR: 85; Size: 3 $\sim$ 20mm95.29 - 12.90 0.85 - 12.56s 结节检测 形态学 孙娟等[86] 2014 私有slices: 30 98.2 - 8.8 - - 3.76ms/slice 结节检测 聚类方法 王凯等[69] 2014 公开LIDC; scans: 17;
LR: 30; Size: 3 $\sim$ 30mm100 - 6.7/scan - - - 结节检测 形态结构 申正文等[75] 2016 私有4DCT;
两组10相位- - - - 0.868
0.830- 肿瘤分割 跟踪方法 Camarlinghi等[6] 2012 公开LIDC; scans: 69
ST $＜$ 2mm; LR: 11485 - 25FP/scan - - - 结节检测 混合方法 Choi等[5] 2012 公开LIDC; scans:32;LR:76;
Slices:5453;Size:3 $\sim$ 30mm94.1 - 5.45/scan 0.967 - - 结节检测 遗传算法 Elizabeth等[17] 2012 私有slices: 1714 92.31 - 94.92 - - - 结节检测 区域增长 Netto等[18] 2012 私有exams: 29; LR: 48 86 91 0.138FP/exam - - - 结节检测 聚类方法 Song等[61] 2013 私有scans: 23 - - - - 0.81 - 结节/肿块 概率方法 Badura等[57] 2014 公开LIDC-IDRI; LR: 551 95.5 - 9.15 - - - 结节分割 聚类方法 Guo等[62] 2014 私有scans: 7 - - - - 0.85 - 肿瘤分割 概率方法 Jacobs等[48] 2014 公开scans: 318; LR:339;
Size:5 $\sim$ 33mm80 - 1.0/scan - - 122s/scan 结节检测 混合方法 Santos等[16] 2014 公开LIDC; scans:28;LR:252 90.6 85 1.17/scan - - - 结节检测 混合方法 Wang等[94] 2015 私有scans:103; nodules:127 - - 4FP/scan FROC:
0.88- - 结节检测 混合方法 Yong等[70] 2014 私有scans: 50 - - - - 91.2 - 肿瘤分割 形态结构 Demir等[91] 2015 公开LIDC-IDRI; scans: 200
Slices: 2775898.03 - 2.45/scan - - - 结节检测 统计模
式识别李阳等[79] 2013 私有scans: 20 100 95 - - - - 结节检测 统计模
式识别Lassen等[82] 2015 公开LIDC-IDRI; LR: 59 - - - - 0.52 结节分割 混合方法 Lu等[83] 2015 公开LIDC; scans:294;LR:631 85.2 - 3.31 - - - 结节检测 混合方法 Messay等[21] 2015 公开LIDC-IDRI; scans:456 - - - - 77.8 结节分割 混合方法 Qiang等[84] 2015 私有scans: 25; LR: 280 88.09 - 10.32 - 89.8 - 结节分割 形态结构 Setio等[77] 2015 LP: 238 98.3 - 4.0/scan - - - 结节检测 统计模
式识别Erdal等[88] 2015 公开LIDC; slices:138 - - - 0.9679 - - 结节检测 统计模
式识别Sheeraz等[22] 2016 公开LIDC; scans: 84 95.31 99.73 - - - - 结节检
测、分类统计模
式识别Senthilkumar等[72] 2016 私有LR: 25 88 84.05 2.05/scan - - - 肿瘤检测 区域增长 Manikandan等[49] 2016 私有scans: 106; LR: 801 100 93 0.38/patient - - - 肿瘤检测 数学
形态学Shin等[96] 2016 公开, 数据来自文献[107-108] - - TPR =0.85;
3FP/scan0.95 - - 淋巴结和
IDL检测深度学习 Dou等[98] 2016 公开LIDC-IDRI; scans:888 92.2 - 8FP/scan - - - 结节检测 深度学习 Cheng等[99] 2016 公开LIDC-IDRI;
良恶性结节:各70090.8±5.3 98.1±2.2 - 0.984±0.015 - - 结节分类 深度学习 Arnaud等[100] 2016 公开LIDC-IDRI; scans: 888 90.1 - 4/scan 0.996 - - 结节检测 深度学习 Bram等[101] 2015 公开LIDC-IDRI; scans: 865 77(上限78) - 8FP/scan - - - 结节分类 深度学习
拿来主义Francesco等[102] 2015 公开NELSON; nodules:4026 - - - 0.868 - - 结节分类 深度学习
拿来主义Thomas等[103] 2014 私有200002Dimagepatches;
5个类别- - 误分类率:
5.3- - - 结节分类 深度学习
半监督Song等[73] 2016 公开LIDC-IDRI; LR: 850;
私有LR: 12196.2 - 9.1 - - 8s 结节, 肿瘤 区域增长 刘峡壁等[109] 2015 公开LISS; scans: 271 70.2 97.2 - - - - 9类征
象分类混合方法 Guo等[46] 2016 公开LISS; 2D GGO: 45;
3D GGO 19100 33.13 - - - - GGO分类 阈值法
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