肺结节图像的计算机自动检测研究进展
Research Progress of Computer Automatic Detection of Pulmonary Nodules in Images
DOI: 10.12677/CSA.2021.111016, PDF,    国家自然科学基金支持
作者: 陈 辰, 苗 军：北京信息科技大学，网络文化与数字传播北京市重点实验室，北京；齐洪钢, 刘 艳：中国科学院大学，计算机科学与技术学院，北京；郭志军, 刘海涛：华北石油管理局总医院，河北 任丘
关键词: 肺结节；检测；分割；假阳性剔除；Pulmonary Nodules； Detection； Segmentation； False Positive Reduction
摘要: 肺结节的早期筛查对肺癌的预防和治疗具有重要的意义。采用计算机断层扫描(CT)可以提高肺癌早期筛查中肺结节的检出率，是目前最为安全有效的肺癌筛查方式之一。随着CT数据日益增长，使用计算机辅助诊断(CAD)系统可以极大减少放射科医生的工作量以及降低漏检率。结合近年来肺结节检测的相关文献，对该领域的研究进展进行了综述。首先介绍了目前广泛应用的胸部CT数据集以及肺结节检测相关的评价指标。然后介绍并分析了肺结节检测框架中一些有效的方法，包括肺实质分割、候选结节检测以及假阳性剔除。最后，讨论了该领域存在的挑战以及未来发展的方向，为今后该领域的研究人员提供参考。
Abstract: Early screening of pulmonary nodules is of great significance for the prevention and treatment of lung cancer. Computed tomography (CT) can improve the detection rate of pulmonary nodules in early lung cancer screening, which is one of the most effective lung cancer screening methods. With the increasing of CT data, the use of computer-aided diagnosis (CAD) system can greatly reduce the workload of radiologists and reduce the rate of missed detection. This paper reviews the research progress of pulmonary nodule detection in recent years. Firstly, this paper introduces the widely used chest CT open dataset and the evaluation metrics related to the detection of pulmonary nodule. Then it introduces and analyzes some effective methods in the framework of pulmonary nodule detection, including lung parenchyma segmentation, candidate nodule detection and false positive reduction after detection. Finally, the challenges and future development of this field are discussed, which can provide reference for future researchers in this field.
文章引用：陈辰, 苗军, 齐洪钢, 刘艳, 郭志军, 刘海涛. 肺结节图像的计算机自动检测研究进展[J]. 计算机科学与应用, 2021, 11(1): 143-155. https://doi.org/10.12677/CSA.2021.111016

参考文献

[1] Bray, F., Ferlay, J., Soerjomataram, I., et al. (2018) Global Cancer Statistics 2018: GLOBOCA Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians, 68, 394-424. https://www.ncbi.nlm.nih.gov/pubmed/30207593 [Google Scholar] [CrossRef] [PubMed]
[2] 周清华, 范亚光, 王颖, 乔友林, 王贵齐, 黄云超, 王新允, 吴宁, 张国桢, 郑向鹏, 步宏. 中国肺部结节分类、诊断与治疗指南(2016年版) [J]. 中国肺癌杂志, 2016, 19(12): 793-798.
[3] National Lung Screening Trial Research Team (2011) Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening. New England Journal of Medicine, 365, 395-409. [Google Scholar] [CrossRef] 
[4] Messay, T., Hardie, R.C. and Rogers, S.K. (2010) A New Compu-tationally Efficient CAD System for Pulmonary Nodule Detection in CT Imagery. Medical Image Analysis, 14, 390-406. [Google Scholar] [CrossRef] [PubMed]
[5] Murphy, K., van Ginneken, B., Schilham, A.M.R., et al. (2009) A Large-Scale Evaluation of Automatic Pulmonary Nodule Detection in Chest CT Using Local Image Features and k-Nearest-Neighbour Classification. Medical Image Analysis, 13, 757-770. [Google Scholar] [CrossRef] [PubMed]
[6] Hardie, R.C., Rogers, S.K., Wilson, T., et al. (2008) Performance Analysis of a New Computer Aided Detection System for Identifying Lung Nodules on Chest Radiographs. Medical Image Analysis, 12, 240-258. [Google Scholar] [CrossRef] [PubMed]
[7] Lewis, D. (1991) Evaluating Text Categorization. In: Proceedings of Speech and Natural Language Workshop, Morgan Kaufmann, Burlington, 312-318. [Google Scholar] [CrossRef] 
[8] Van Ginneken, B., Armato III, S.G., de Hoop, B., et al. (2010) Com-paring and Combining Algorithms for Computer-Aided Detection of Pulmonary Nodules in Computed Tomography Scans: The ANODE09 Study. Medical Image Analysis, 14, 707-722. [Google Scholar] [CrossRef] [PubMed]
[9] Mansoor, A., Bagci, U., Foster, B., et al. (2015) Segmentation and Image Analysis of Abnormal Lungs at CT: Current Approaches, Challenges, and Future Trends. RadioGraphics, 35, 1056-1076. [Google Scholar] [CrossRef] [PubMed]
[10] Hu, S., Hoffman, E.A. and Reinhardt, J.M. (2001) Auto-matic Lung Segmentation for Accurate Quantitation of Volumetric X-Ray CT Images. IEEE Transactions on Medical Imaging, 20, 490-498. [Google Scholar] [CrossRef] [PubMed]
[11] Shariaty, F., Hosseinlou, S. and Rud, V.Y. (2019) Automatic Lung Segmentation Method in Computed Tomography Scans. Journal of Physics: Conference Series, 1236, Article ID: 012028. [Google Scholar] [CrossRef] 
[12] Zhang, W., Zhang, X., Zhao, J., et al. (2017) A Segmenta-tion Method for Lung Nodule Image Sequences Based on Superpixels and Density-Based Spatial Clustering of Applica-tions with Noise. PLoS ONE, 12, e0184290. [Google Scholar] [CrossRef] [PubMed]
[13] Vincent, L. and Soille, P. (1991) Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations. IEEE Transactions on Pattern Analysis & Machine Intelli-gence, 13, 583-598. [Google Scholar] [CrossRef] 
[14] Adams, R. and Bischof, L. (1994) Seeded Region Growing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16, 641-647. [Google Scholar] [CrossRef] 
[15] Wu, J., Poehlman, S., Noseworthy, M.D., et al. (2008) Texture Feature Based Automated Seeded Region Growing in Abdominal MRI Segmentation. 2008 International Conference on Bio-Medical Engineering and Informatics IEEE, Vol. 2, 263-267. [Google Scholar] [CrossRef] 
[16] Zhao, J., Ji, G., Han, X., et al. (2016) An Automated Pulmonary Parenchyma Segmentation Method Based on an Improved Re-gion Growing Algorithmin PET-CT Imaging. Frontiers of Computer Science, 10, 189-200. [Google Scholar] [CrossRef] 
[17] Shojaii, R., Alirezaie, J. and Babyn, P. (2005) Automatic Lung Segmentation in CT Images Using Watershed Transform. IEEE International Conference on Image Processing, 2, II-1270. [Google Scholar] [CrossRef] 
[18] Grady, L. (2006) Random Walks for Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28, 1768-1783. [Google Scholar] [CrossRef] 
[19] Qin, C., Zhang, G., Zhou, Y., et al. (2014) Integration of the Sali-ency-Based Seed Extraction and Random Walks for Image Segmentation. Neurocomputing, 129, 378-391. [Google Scholar] [CrossRef] 
[20] Udupa, J.K. and Samarasekera, S. (1996) Fuzzy Connectedness and Object Definition: Theory, Algorithms, and Applications in Image Segmentation. Graphical Models and Image Pro-cessing, 58, 246-261. [Google Scholar] [CrossRef] 
[21] Harrison, A.P., Xu, Z., George, K., et al. (2017) Progressive and Multi-Path Holistically Nested Neural Networks for Pathological Lung Segmentation from CT Images. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Cham, 621-629. [Google Scholar] [CrossRef] 
[22] Xie, S. and Tu, Z. (2015) Holistically-Nested Edge Detection. Proceedings of the IEEE International Conference on Computer Vision, Santiago, 7-13 December 2015, 1395-1403. [Google Scholar] [CrossRef] 
[23] Dice, L.R. (1945) Measures of the Amount of Ecologic Association between Species. Ecology, 26, 297-302. [Google Scholar] [CrossRef] 
[24] Ronneberger, O., Fischer, P. and Brox, T. (2015) U-Net: Convolutional Networks for Biomedical Image Segmentation. International Conference on Medical Image Computing and Comput-er-Assisted Intervention, Springer, Cham, 234-241. [Google Scholar] [CrossRef] 
[25] Alom, M.Z., Yakopcic, C., Hasan, M., et al. (2019) Recurrent Residual U-Net for Medical Image Segmentation. Journal of Medical Imaging, 6, Article ID: 014006. [Google Scholar] [CrossRef] 
[26] Shaziya, H., Shyamala, K. and Zaheer, R. (2018) Automatic Lung Segmentation on Thoracic CT Scans Using u-Net Convolutional Network. 2018 International Conference on Communi-cation and Signal Processing (ICCSP) IEEE, Chennai, 3-5 April 2018, 643-647. [Google Scholar] [CrossRef] 
[27] Pang, T., Guo, S., Zhang, X., et al. (2019) Automatic Lung Segmentation Based on Texture and Deep Features of HRCT Images with Interstitial Lung Disease. BioMed Research International, 2019, Article ID: 2045432. [Google Scholar] [CrossRef] [PubMed]
[28] He, K., Zhang, X., Ren, S., et al. (2016) Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, 27-30 June 2016, 770-778. [Google Scholar] [CrossRef] 
[29] Liang, M. and Hu, X. (2015) Recurrent Convolutional Neural Net-work for Object Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, 7-12 June 2015, 3367-3375.
[30] Lung Segmentation Dataset.
https://www.kaggle.com/kmader/finding-lungs-in-ct-data/data
[31] Han, H., Li, L., Han, F., et al. (2014) Fast and Adaptive Detection of Pulmonary Nodules in Thoracic CT Images Using a Hierarchical Vector Quantization Scheme. IEEE Journal of Biomedical and Health Informatics, 19, 648-659. [Google Scholar] [CrossRef] 
[32] Brown, M.S., Lo, P., Goldin, J.G., et al. (2014) Toward Clini-cally Usable CAD for Lung Cancer Screening with Computed Tomography. European Radiology, 24, 2719-2728. [Google Scholar] [CrossRef] [PubMed]
[33] Lin, T.Y., Dollár, P., Girshick, R., et al. (2017) Feature Pyramid Networks for Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Hon-olulu, 21-26 July 2017, 2117-2125. [Google Scholar] [CrossRef] 
[34] Girshick, R., Donahue, J., Darrell, T., et al. (2014) Rich Feature Hi-erarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, 23-28 June 2014, 580-587. [Google Scholar] [CrossRef] 
[35] Ren, S., He, K., Girshick, R., et al. (2016) Faster r-cnn: Towards Re-al-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis and Machine Intelli-gence, 39, 1137-1149. [Google Scholar] [CrossRef] 
[36] Ding, J., Li, A., Hu, Z., et al. (2017) Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Cham, 559-567. [Google Scholar] [CrossRef] 
[37] Simonyan, K. and Zisserman, A. (2014) Very Deep Convolu-tional Networks for Large-Scale Image Recognition.
[38] Liao, F., Liang, M., Li, Z., et al. (2019) Evaluate the Malig-nancy of Pulmonary Nodules Using the 3-d Deep Leaky Noisy-Or Network. IEEE Transactions on Neural Networks and Learning Systems, 30, 3484-3495. [Google Scholar] [CrossRef] 
[39] Zhu, W., Liu, C., Fan, W., et al. (2018) Deeplung: Deep 3d Dual Path Nets for Automated Pulmonary Nodule Detection and Classification. 2018 IEEE Winter Conference on Appli-cations of Computer Vision (WACV), Lake Tahoe, 12-15 March 2018, 673-681. [Google Scholar] [CrossRef] 
[40] Tang, H., Zhang, C. and Xie, X. (2019) Nodulenet: Decoupled False Positive Reduction for Pulmonary Nodule Detection and Segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Cham, 266-274. [Google Scholar] [CrossRef] 
[41] Cheng, B., Wei, Y., Shi, H., et al. (2018) Revisiting rcnn: On Awakening the Classification Power of Faster rcnn. Proceedings of the European Conference on Computer Vision (ECCV), Munich, 8-14 September 2018, 453-468. [Google Scholar] [CrossRef] 
[42] Ozdemir, O., Russell, R.L. and Berlin, A.A. (2019) A 3D Probabilistic Deep Learning System for Detection and Diagnosis of Lung Cancer Using Low-Dose CT Scans. IEEE Transactions on Medical Imaging, 39, 1419-1429. [Google Scholar] [CrossRef] 
[43] Milletari, F., Navab, N. and Ahmadi, S.A. (2016) V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. 2016 Fourth International Conference on 3D Vision (3DV) IEEE, Stanford, 25-28 October 2016, 565-571. [Google Scholar] [CrossRef] 
[44] Masood, A., Yang, P., Sheng, B., et al. (2019) Cloud-Based Automated Clinical Decision Support System for Detection and Diagnosis of Lung Cancer in Chest CT. IEEE Journal of Transla-tional Engineering in Health and Medicine, 8, 1-13. [Google Scholar] [CrossRef] 
[45] Jacobs, C., van Rikxoort, E.M., Twellmann, T., et al. (2014) Automatic Detection of Subsolid Pulmonary Nodules in Thoracic Computed Tomography Images. Medical Image Analysis, 18, 374-384. [Google Scholar] [CrossRef] [PubMed]
[46] Naqi, S.M., Sharif, M. and Lali, I.U. (2019) A 3D Nodule Can-didate Detection Method Supported by Hybrid Features to Reduce False Positives in Lung Nodule Detection. Multimedia Tools and Applications, 78, 26287-26311. [Google Scholar] [CrossRef] 
[47] Liu, J., Cao, L., Akin, O., et al. (2019) Accurate and Robust Pulmonary Nodule Detection by 3D Feature Pyramid Network with Self-Supervised Feature Learning.
[48] Davis, J.W. (2001) Hierarchical Motion History Images for Recognizing Human Motion. Proceedings IEEE Workshop on Detection and Recognition of Events in Video, Vancouver, 8 July 2001, 39-46.
[49] Zuo, W., Zhou, F., Li, Z., et al. (2019) Mul-ti-Resolution CNN and Knowledge Transfer for Candidate Classification in Lung Nodule Detection. IEEE Access, 7, 32510-32521. [Google Scholar] [CrossRef] 
[50] Dou, Q., Chen, H., Yu, L., et al. (2016) Mul-tilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection. IEEE Transactions on Bio-medical Engineering, 64, 1558-1567. [Google Scholar] [CrossRef] 
[51] Kim, B.C., Yoon, J.S., Choi, J.S., et al. (2019) Multi-Scale Gradual Integration CNN for False Positive Reduction in Pulmonary Nodule Detection. Neural Networks, 115, 1-10. [Google Scholar] [CrossRef] [PubMed]
[52] Armato III, S.G., Hadjiiski, L.M., Tourassi, G.D., et al. (2015) Special Section Guest Editorial: LUNGx Challenge for Computerized Lung Nodule Classification: Reflections and Les-sons Learned. Journal of Medical Imaging, 2, Article ID: 020103. [Google Scholar] [CrossRef] 
[53] Dolejsi, M., Kybic, J., Polovincak, M., et al. (2009) The Lung Time: Annotated Lung Nodule Dataset and Nodule Detection Framework. Medical Imaging 2009: Computer-Aided Diagnosis, International Society for Optics and Photonics, Vol. 7260, 72601U. [Google Scholar] [CrossRef] 
[54] Mak, R.H., Endres, M.G., Paik, J.H., et al. (2019) Use of Crowd Innovation to Develop an Artificial Intelligence-Based solu-tion for Radiation Therapy Targeting. JAMA Oncology, 5, 654-661. [Google Scholar] [CrossRef] [PubMed]
[55] Creswell, A., White, T., Dumoulin, V., et al. (2018) Generative Adversarial Networks: An Overview. IEEE Signal Processing Magazine, 35, 53-65. [Google Scholar] [CrossRef] 
[56] Du, J., Li, W., Lu, K., et al. (2016) An Overview of Multi-Modal Medical Image Fusion. Neurocomputing, 215, 3-20. [Google Scholar] [CrossRef]