CT图像肺及肺病变区域分割方法综述

冯龙锋; 陈英; 周滔辉; 胡菲; 易珍

doi:10.11834/jig.210769

综述 | 浏览量 : 0 下载量: 226 CSCD: 4

PDF
导出
分享
收藏
专辑

CT图像肺及肺病变区域分割方法综述
Review of human lung and lung lesion regions segmentation methods based on CT images
2022年27卷第3期页码：722-749
收稿：2021-09-08，

修回：2021-10-31，

录用：2021-11-7，

纸质出版：2022-03-16
DOI： 10.11834/jig.210769
稿件说明：

移动端阅览

冯龙锋, 陈英, 周滔辉, 胡菲, 易珍. CT图像肺及肺病变区域分割方法综述[J]. 中国图象图形学报, 2022,27(3):722-749. DOI： 10.11834/jig.210769.

Longfeng Feng, Ying Chen, Taohui Zhou, Fei Hu, Zhen Yi. Review of human lung and lung lesion regions segmentation methods based on CT images[J]. Journal of Image and Graphics, 2022, 27(3): 722-749. DOI： 10.11834/jig.210769.

摘要

计算机断层扫描(computed tomography，CT)技术能为新冠肺炎(corona virus disease 2019，COVID-19)和肺癌等肺部疾病的诊断与治疗提供更全面的信息，但是由于肺部疾病的类型多样且复杂，使得对肺CT图像进行高质量的肺病变区域分割成为计算机辅助诊断的重难点问题。为了对肺CT图像的肺及肺病变区域分割方法的现状进行全面研究，本文综述了近年国内外发表的相关文献：对基于区域和活动轮廓的肺CT图像传统分割方法的优缺点进行比较与总结，传统的肺CT图像分割方法因其实现原理简单且分割速度快等优点，早期使用较多，但其存在分割精度不高的缺点，目前仍有不少基于传统方法的改进策略；重点分析了基于卷积神经网络(convolutional neural network，CNN)、全卷积网络(fully convolutional network，FCN)、U-Net和生成对抗网络(generative adversarial network，GAN)的肺CT图像分割网络结构改进模型的研究进展，基于深度学习的分割方法具有分割精度高、迁移学习能力强和鲁棒性高等优点，特别是在辅助诊断COVID-19病例时，基于深度学习方法的性能明显优于基于传统方法的性能；介绍肺及肺病变区域分割的常用数据集和评价指标，在解决如COVID-19数据样本量少等问题时，使用GAN以合成高质量的对抗性图像用以扩充数据集，从而增加训练样本的数量和多样性；讨论了肺CT图像的肺及肺病变区域的高精度分割策略的研究趋势、现有挑战和未来的研究方向。

Abstract

Lung disease like corona virus disease 2019(COVID-19) and lung cancer endanger the health of human beings. Early screening and treatment can significantly decrease the mortality of lung diseases. Computed tomography (CT) technology can be an effective information collection method for the diagnosis and treatment of lung diseases. CT-based lung lesion region image segmentation is a key step in lung disease screening. High quality lung lesion region segmentation can effectively improve the level of early stage diagnosis and treatment of lung diseases. However

high-quality lung lesion region segmentation in lung CT images has become a challenging issue in computer-aided diagnosis due to the diversity and complexity of lung diseases. Our research reviews the relevant literature recently. First

it is compared and summarized the pros and cons of traditional segmentation methods of lung CT image based on region and active contour. The region-based method uses the similarity and difference of features to guide image segmentation

mainly including threshold method

region growth method

clustering method and random walk method. The active-contour-based method is to set an initial contour line with decreasing energy. The contour line deforms in the internal energy derived from its own characteristics and the external energy originated from image characteristics. Its movement is in accordance with the principle of minimum energy until the energy function is in minimization and the contour line stops next to the boundary of lung region. The active contour method is divided into parametric active contour method and geometric active contour method in terms of the contour curve analysis. Low segmentation accuracy lung CT image segmentation methods are widely used in the early stage diagnosis. Next

the improved model analysis of lung CT image segmentation network structure is based on convolutional neural networks (CNNs)

fully convolutional networks (FCNs)

and generative adversarial network (GAN). In respect of the CNN-based deep learning segmentation methods

the segmentation methods of lung and lung lesion region can be divided into two-dimensional and three-dimensional methods in terms of the dimension of convolution kernel

the segmentation methods of lung and lung lesion region can also be divided into two-dimensional and three-dimensional methods based on the dimension of convolution kernel for the FCN-based deep learning segmentation methods. In respect of the U-Net based lung CT image segmentation methods

it can be divided into solo network lung CT image segmentation method and multi network lung CT image segmentation method according to the form of U-Net architecture. Due to the CT image containing COVID-19 infection area is very different from the ordinary lung CT imageand the differentiated segmentation characteristics of the two in the same network

the solo network lung CT image segmentation method can be analyzed that whether the data-set contains COVID-19 or not. The multi-network lung CT image segmentation method can be divided into cascade U-Net and dual path U-Net based on the option of serial mode or parallel mode. For the GAN-based lung CT image segmentation methods

it can be divided into GAN models based on network architecture

generator and other methods according to the ways to improve the different architectures of GAN. Deep-learning-based segmentation method has the advantages of high segmentation accuracy

strong transfer learning ability and high robustness. In particular

the auxiliary diagnosis of COVID-19 cases analysis is significantly qualified based on deep learning. Next

the common datasets and evaluation indexes of lung and lung lesion region segmentation are illustrated

including almost 10 lung CT open datasets

such as national lung screening test(NLST) dataset

computer vision and image analysis international early lung cancer action plan database(VIA/I-ELCAP) dataset

lung image database consortium and image database resource initiative(LIDC-IDRI) dataset and Nederlands-Leuvens Longkanker Screenings Onderzoek(NELSON) dataset

and 7 COVID-19 lung CT datasets analysis. It also demonstrates that the related lung CT images datasets is provided based on five large-scale competitions

including TIANCHI dataset

lung nodule analysis 16(LUNA16) dataset

Lung Nodule Database(LNDb) dataset

Kaggle Data Science Bowl 2017(Kaggle DSB) 2017 dataset and Automatic Nodule Detection 2009(ANODE09) dataset

respectively. Our 8 evaluation index is commonly used to evaluate the quality of lung CT image segmentation model

including involved Dice similarity coefficient

Jaccard similarity coefficient

accuracy

precision

false positive rate

false negative rate

sensitivity and specificity

respectively. To increase the number and diversity of training samples

GAN is used to synthesize high-quality adversarial images to expand the dataset. At the end

the prospects

challenges and potentials of CT-based high-precision segmentation strategies are critical reviewed for lung and lung lesion regions. Because the special structure of U-Net can effectively extract target features and restore the information loss derived from down sampling

it does not need a large number of samples for training to achieve high segmentation effect. Therefore

it is necessary to segment lung and lung lesions based on U-Net. The integration of GAN and U-Net is to improve the segmentation accuracy of lung and lung lesion areas. GAN-based network architecture is to extend the dataset for good training quality. The further U-Net application has its priority for qualified segmentation consistently.

关键词

Keywords

references

Aberle D R, Adams A M, Berg C D, Black W C, Clapp J D, Fagerstrom R M, Gareen I F, Gatsonis C, Marcus P M and Sicks J D. 2011. Reduced lung-cancer mortality with low-dose computed tomographic screening. New England Journal of Medicine, 365(5): 395-409 [DOI: 10.1056/NEJMoa1102873]

Adarsh R, Amarnageswarao G, Pandeeswari R and Deivalakshmi S. 2020. Inception block based residual auto encoder for lung segmentation//Proceedings of the 4th International Conference on Computer, Communication and Signal Processing (ICCCSP). Chennai, India: IEEE: 1-5 [ DOI: 10.1109/ICCCSP49186.2020.9315264 http://dx.doi.org/10.1109/ICCCSP49186.2020.9315264 ]

Afzali A, Babapour Mofrad F and Pouladian M. 2021. 2D statistical lung shape analysis using chest radiographs: modelling and segmentation. Journal of Digital Imaging, 34(3): 523-540 [DOI: 10.1007/s10278-021-00440-7]

Ai T, Yang Z L, Hou H Y, Zhan C N, Chen C, Lv W Z, Tao Q, Sun Z Y and Xia L M. 2020. Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology, 296(2): E32-E40 [DOI: 10.1148/radiol.2020200642]

Akila A S, Anitha J and Dinesh P J. 2020. Automatic lung segmentation in low-dose chest CT scans using convolutional deep and wide network (CDWN). Neural Computing and Applications, 32(20): 15845-15855 [DOI: 10.1007/s00521-018-3877-3]

Ali R, Hardie R C and Ragb H K. 2020. Ensemble lung segmentation system using deep neural networks//2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR). Washington, USA: IEEE: 1-5 [ DOI: 10.1109/AIPR50011.2020.9425311 http://dx.doi.org/10.1109/AIPR50011.2020.9425311 ]

Anirudh R, Thiagarajan J J, Bremer T and Kim H. 2016. Lung nodule detection using 3D convolutional neural networks trained on weakly labeled data//Proceedings Volume 9785, Medical Imaging 2016: Computer-Aided Diagnosis. San Diego, USA: SPIE: #978532 [ DOI: 10.1117/12.2214876 http://dx.doi.org/10.1117/12.2214876 ]

Anthimopoulos M, Christodoulidis S, Ebner L, Geiser T, Christe A and Mougiakakou S. 2019. Semantic segmentation of pathological lung tissue with dilated fully convolutional networks. IEEE Journal of Biomedical and Health Informatics, 23(2): 714-722 [DOI: 10.1109/JBHI.2018.2818620]

Bressem K K, Niehues S M, Hamm B, Makowski M R, Vahldiek J L and Adams L C. 2021. 3D U-Net for segmentation of COVID-19 associated pulmonary infiltrates using transfer learning: state-of-the-art results on affordable hardware[EB/OL]. [2021-08-20] . https://arxiv.org/pdf/2101.09976.pdf https://arxiv.org/pdf/2101.09976.pdf

Bruntha P M, Pandian S I A and Mohan P. 2019. Active contour model (without edges) based pulmonary nodule detection in low dose CT images//Proceedings of the 2nd International Conference on Signal Processing and Communication (ICSPC). Coimbatore, India: IEEE: 222-225 [ DOI: 10.1109/ICSPC46172.2019.8976813 http://dx.doi.org/10.1109/ICSPC46172.2019.8976813 ]

Bu T, Yang Z Y, Jiang S, Zhang G B, Zhang H Y and Wei L. 2021. 3D conditional generative adversarial network-based synthetic medical image augmentation for lung nodule detection. International Journal of Imaging Systems and Technology, 31(2): 670-681 [DOI: 10.1002/ima.22511]

Cao H C, Liu H, Song E M, Hung C C, Ma G Z, Xu X Y, Jin R C and Lu J G. 2020. Dual-branch residual network for lung nodule segmentation. Applied Soft Computing, 86: #105934 [DOI: 10.1016/j.asoc.2019.105934]

Chen S Q, Zhong X, Hu S Y, Dorn S, Kachelrieβ M, Lell M and Maier A. 2020a. Automaticmulti-organ segmentation in dual-energy CT (DECT) with dedicated 3D fully convolutional DECT networks. Medical Physics, 47(2): 552-562 [DOI: 10.1002/mp.13950]

Chen W, Wei H F, Peng S T, Sun J W, Qiao X and Liu B Q. 2019. HSN: hybrid segmentation network for small cell lung cancer segmentation. IEEE Access, 7: 75591-75603 [DOI: 10.1109/ACCESS.2019.2921434]

Chen Y, Wang Y R, Hu F and Wang D. 2020b. A lung dense deep convolution neural network for robust lung parenchyma segmentation. IEEE Access, 8: 93527-93547 [DOI: 10.1109/ACCESS.2020.2993953]

Chong S Y, Tan M K, Yeo K B, Ibrahim M Y, Hao X X and Teo K T K. 2019. Segmenting nodules of lung tomography image with level set algorithm and neural network//Proceedings of the 7th IEEE Conference on Systems, Process and Control (ICSPC). Melaka, Malaysia: IEEE: 161-166 [ DOI: 10.1109/ICSPC47137.2019.9067987 http://dx.doi.org/10.1109/ICSPC47137.2019.9067987 ]

Cressman S, Peacock S J, Tammemägi M C, Evans W K, Leighl N B, Goffin J R, Tremblay A, Liu G, Manos D, MacEachern P, Bhatia R, Puksa S, Nicholas G, McWilliams A, Mayo J R, Yee J, English J C, Pataky R, McPherson E, Atkar-Khattra S, Johnston M R, Schmidt H, Shepherd F A, Soghrati K, Amjadi K, Burrowes P, Couture C, Sekhon H S, Yasufuku K, Goss G, Ionescu D N, Hwang D M, Martel S, Sin D D, Tan W C, Urbanski S, Xu Z L, Tsao M S and Lam S. 2017. The cost-effectiveness of high-risk lung cancer screening and drivers of program efficiency. Journal of Thoracic Oncology, 12(8): 1210-1222 [DOI: 10.1016/j.jtho.2017.04.021]

Dong T, Wei L and Nie S D. 2021. Research progress of lung nodule segmentation based on CT images. Journal of Image and Graphics, 26(4): 751-765

董婷, 魏珑, 聂生东. 2021. CT影像肺结节分割研究进展. 中国图象图形学报, 26(4): 751-765[DOI: 10.11834/jig.200201]

Dong X, Lei Y, Wang T H, Thomas M, Tang L, Curran W J, Liu T and Yang X F. 2019. Automatic multiorgan segmentation in thorax CT images using U-Net-GAN. Medical Physics, 46(5): 2157-2168 [DOI: 10.1002/mp.13458]

Dutande P, Baid U and Talbar S. 2021. LNCDS: a 2D-3D cascaded CNN approach for lung nodule classification, detection and segmentation. Biomedical Signal Processing and Control, 67: #102527 [DOI: 10.1016/j.bspc.2021.102527]

Fan D P, Zhou T, Ji G P, Zhou Y, Chen G, Fu H Z, Shen J B and Shao L. 2020. Inf-Net: automatic COVID-19 lung infection segmentation from CT images. IEEE Transactions on Medical Imaging, 39(8): 2626-2637 [DOI: 10.1109/TMI.2020.2996645]

Fang Y C, Zhang H Q, Xie J C, Lin M J, Ying L J, Pang P P and Ji W B. 2020. Sensitivity of chest CT for COVID-19: comparison to RT-PCR. Radiology, 296(2): E115-E117 [DOI: 10.1148/radiol.2020200432]

Goodfellow I J, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A and Bengio Y. 2014. Generative adversarial nets [EB/OL]. [2021-08-20] . https://arxiv.org/pdf/1411.1784 https://arxiv.org/pdf/1411.1784 .

Guo W, Zhou H X, Gong Z X and Zhang G D. 2021. Double U-Nets for image segmentation by integrating the region and boundary information. IEEE Access, 9: 69382-69390 [DOI: 10.1109/ACCESS.2021.3075294]

Hamidian S, Sahiner B, Petrick N and Pezeshk A. 2017. 3D convolutional neural network for automatic detection of lung nodules in chest CT//Proceedings Volume 10134, Medical Imaging 2017: Computer-Aided Diagnosis. Orlando, USA: SPIE: #1013409 [ DOI: 10.1117/12.2255795 http://dx.doi.org/10.1117/12.2255795 ]

Han C, Kitamura Y, Kudo A, Ichinose A, Rundo L, Furukawa Y, Umemoto K, Li Y Z and Nakayama H. 2019. Synthesizing diverse lung nodules wherever massively: 3D multi-conditional GAN-based CT image augmentation for object detection//Proceedings of 2019 International Conference on 3D Vision (3DV). Quebec City, Canada: IEEE: 729-737 [ DOI: 10.1109/3DV.2019.00085 http://dx.doi.org/10.1109/3DV.2019.00085 ]

He K L, Zhao W, Xie X Z, Ji W, Liu M X, Tang Z Y, Shi Y H, Shi F, Gao Y, Liu J, Zhang J F and Shen D G. 2021. Synergistic learning of lung lobe segmentation and hierarchical multi-instance classification for automated severity assessment of COVID-19 in CT images. Pattern Recognition, 113: #107828 [DOI: 10.1016/j.patcog.2021.107828]

Henley S J, Ward E M, Scott S, Ma J M, Anderson R N, Firth A U, Thomas C C, Islami F, Weir H K, Lewis D R, Sherman R L, Wu M X, Benard V B, Richardson L C, Jemal A, Cronin K and Kohler B A. 2020. Annual report to the nation on the status of cancer, part I: national cancer statistics. Cancer, 126(10): 2225-2249 [DOI: 10.1002/cncr.32802]

Hesamian M H, Jia W J, He X J and Kennedy P J. 2019. Atrous convolution for binary semantic segmentation of lungnodule//ICASSP 2019—2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Brighton, UK: IEEE: 1015-1019 [ DOI: 10.1109/ICASSP.2019.8682220 http://dx.doi.org/10.1109/ICASSP.2019.8682220 ]

Hossain S, Najeeb S, Shahriyar A, Abdullah Z R and Haque M A. 2019. A pipeline for lung tumor detection and segmentation from CT scans using dilated convolutional neural networks//ICASSP 2019—2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Brighton, UK: IEEE: 1348-1352 [ DOI: 10.1109/ICASSP.2019.8683802 http://dx.doi.org/10.1109/ICASSP.2019.8683802 ]

Huang S, Han X W, Fan J F, Chen J, Du L, Gao W W, Liu B, Chen Y, Liu X X, Wang Y G, Ai D N, Ma G L and Yang J. 2021. Anterior mediastinal lesion segmentation based on two-stage 3D ResUNet with attention gates and lung segmentation. Frontiers in Oncology, 10: #618357 [DOI: 10.3389/fonc.2020.618357]

Huang X, Sun W Q, Tseng T L, Li C Q and Qian W. 2019. Fast and fully-automated detection and segmentation of pulmonary nodules in thoracic CT scans using deep convolutional neural networks. Computerized Medical Imaging and Graphics, 74: 25-36 [DOI: 10.1016/j.compmedimag.2019.02.003]

Jalali Y, Fateh M, Rezvani M, Abolghasemi V and Anisi M H. 2021. ResBCDU-Net: a deep learning framework for lung CT image segmentation. Sensors, 21(1): #268 [DOI: 10.3390/s21010268]

Jiang J, Hu Y C, Liu C J, Halpenny D, Hellmann M D, Deasy J O, Mageras G and Veeraraghavan H. 2019. Multiple resolution residually connected feature streams for automatic lung tumor segmentation from CT images. IEEE Transactions on Medical Imaging, 38(1): 134-144 [DOI: 10.1109/TMI.2018.2857800]

Jin Q G, Cui H, Sun C M, Meng Z P, Wei L Y and Su R. 2021. Domain adaptation based self-correction model for COVID-19 infection segmentation in CT images. Expert Systems with Applications, 176: #114848 [DOI: 10.1016/j.eswa.2021.114848]

Jin T L, Cui H, Zeng S and Wang X Y. 2017. Learning deep spatial lung features by 3D convolutional neural network for early cancer detection//Proceedings of 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA). Sydney, Australia: IEEE: 1-6 [ DOI: 10.1109/DICTA.2017.8227454 http://dx.doi.org/10.1109/DICTA.2017.8227454 ]

Joseph R A N, Zhu H P, Khan A, Zhuang Z M, Yang Z B, Mahesh V G V and Karthik G. 2021. ADID-UNET—a segmentation model for COVID-19 infection from lung CT scans. PeerJ Computer Science, 7: #e349 [DOI: 10.7717/peerj-cs.349]

Kadia D D, Alom M Z, Burada R, Nguyen T V and Asari V K. 2021. R 2 U3D: recurrent residual 3D U-Net for lung segmentation. IEEE Access, 9: 88835-88843 [DOI: 10.1109/ACCESS.2021.3089704]

Kasinathan G, Jayakumar S, Gandomi A H, Ramachandran M, Fong S J and Patan R. 2019. Automated 3-D lung tumor detection and classification by an active contour model and CNN classifier. Expert Systems with Applications, 134: 112-119 [DOI: 10.1016/j.eswa.2019.05.041]

Kopelowitz E and Engelhard G. 2019. Lung nodules detection and segmentation using 3D Mask-RCNN[EB/OL]. [2021-08-20] . https://arxiv.org/pdf/1907.07676v1.pdf https://arxiv.org/pdf/1907.07676v1.pdf

Krizhevsky A, Sutskever I and Hinton G E. 2017. ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6): 84-90 [DOI: 10.1145/3065386]

Kumar S N, Bruntha P M, Daniel S I, Kirubakar J A, Kiruba R E, Sam S and Pandian S I A. 2021. Lung nodule segmentation using UNet//Proceedings of the 7th International Conference on Advanced Computing and Communication Systems (ICACCS). Coimbatore, India: IEEE: 420-424 [ DOI: 10.1109/ICACCS51430.2021.9441977 http://dx.doi.org/10.1109/ICACCS51430.2021.9441977 ]

Lecun Y, Bottou L, Bengio Y and Haffner P. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11): 2278-2324

Lee H, Matin T, Gleeson F and Grau V. 2019. Efficient 3D fully convolutional networks for pulmonary lobe segmentation in CT images[EB/OL]. [2021-08-20] . https://arxiv.org/pdf/1909.07474v1.pdf https://arxiv.org/pdf/1909.07474v1.pdf

Li L Q, Zhao X M, Lu W and Tan S. 2020. Deep learning for variational multimodality tumor segmentation in PET/CT. Neurocomputing, 392: 277-295 [DOI: 10.1016/j.neucom.2018.10.099]

Liu C X and Pang M Y. 2020. Automatic lung segmentation based on image decomposition and wavelet transform. Biomedical Signal Processing and Control, 61: #102032 [DOI: 10.1016/j.bspc.2020.102032]

Liu H, Cao H C, Song E M, Ma G Z, Xu X Y, Jin R C, Jin Y and Hung C C. 2019. A cascaded dual-pathway residual network for lung nodule segmentation in CT images. Physica Medica, 63: 112-121 [DOI: 10.1016/j.ejmp.2019.06.003]

Long J, Shelhamer E and Darrell T. 2015. Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4): 640-651 [DOI: 10.1109/TPAMI.2016.2572683]

Ma J L, Deng Y Y and Ma Z P. 2020. Review of deep learning segmentation methods for CT images of liver tumors. Journal of Image and Graphics, 25(10): 2024-2046

马金林, 邓媛媛, 马自萍. 2020. 肝脏肿瘤CT图像深度学习分割方法综述. 中国图象图形学报, 25(10): 2024-2046[DOI: 10.11834/jig.200234]

Manickavasagam R and Selvan S. 2019. GACM based segmentation method for Lung nodule detection and classification of stages using CT images//Proceedings of the 1st International Conference on Innovations in Information and Communication Technology (ICIICT). Chennai, India: IEEE: 1-5 [ DOI: 10.1109/ICIICT1.2019.8741477 http://dx.doi.org/10.1109/ICIICT1.2019.8741477 ]

Nemoto T, Futakami N, Yagi M, Kumabe A, Takeda A, Kunieda E and Shigematsu N. 2020. Efficacy evaluation of 2D, 3D U-Net semantic segmentation and atlas-based segmentation of normal lungs excluding the trachea and main bronchi. Journal of Radiation Research, 61(2): 257-264 [DOI: 10.1093/jrr/rrz086]

Ng M Y, Lee E Y P, Yang J, Yang F F, Li X, Wang H X, Lui M M S, Lo C S Y, Leung B, Khong P L, Hui C K M, Yuen K Y and Kuo M D. 2020. Imaging profile of the COVID-19 infection: radiologic findings and literature review. Radiology: Cardiothoracic Imaging, 2(1): #e200034 [DOI: 10.1148/ryct.2020200034]

Nie Y, Zhuo D Y, Song G H and Wen S T. 2018. Pulmonary nodule segmentation method of CT images based on 3D-FCN//Web and Big Data, Lecture Notes in Computer Science. Macau, China: Springer: 134-141 [ DOI: 10.1007/978-3-030-01298-4_13 http://dx.doi.org/10.1007/978-3-030-01298-4_13 ]

Nithila E E and Kumar S S. 2019. Segmentation of lung from CT using various active contour models. Biomedical. Signal Processing and Control, 47: 57-62 [DOI: 10.1016/j.bspc.2018.08.008]

Nurfauzi R, Nugroho H A, Ardiyanto I and Frannita E L. 2021. Autocorrection of lung boundary on 3D CT lung cancer images. Journal of King Saud University-Computer and Information Sciences, 33(5): 518-527 [DOI: 10.1016/j.jksuci.2019.02.009]

Park J, Yun J, Kim N, Park B, Cho Y, Park H J, Song M, Lee M and Seo J B. 2020. Fully automated lung lobe segmentation in volumetric chest CT with 3D U-Net: validation with intra-and extra-datasets. Journal of Digital Imaging, 33(1): 221-230 [DOI: 10.1007/s10278-019-00223-1]

Pawar S P and Talbar S N. 2021. LungSeg-Net: lung field segmentation using generative adversarial network. Biomedical Signal Processing and Control, 64: #102296 [DOI: 10.1016/j.bspc.2020.102296]

Prasad J M N, Chakravarthy S and Krishna M V. 2021. A novel approach to CAD for the detection of small cell and non-small cell lung cancers. Materials Today: Proceedings [DOI: 10.1016/j.matpr.2020.12.1064]

Qin Y L, Zheng H, Huang X L, Yang J and Zhu Y M. 2019. Pulmonary nodule segmentation with CT sample synthesis using adversarial networks. Medical Physics, 46(3): 1218-1229 [DOI: 10.1002/mp.13349]

Rakesh S and Mahesh S. 2021. Nodule segmentation of lung CT image for medical applications. Global Transitions Proceedings, 2(1): 80-83 [DOI: 10.1016/j.gltp.2021.01.011]

Ronneberger O, Fischer P and Brox T. 2015. U-Net: convolutional networks for biomedical image segmentation//Proceedings of 18th International Conference on Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015. Munich, Germany: Springer: 234-241 [ DOI: 10.1007/978-3-319-24574-4_28 http://dx.doi.org/10.1007/978-3-319-24574-4_28 ]

Roth H R, Oda H, Zhou X R, Shimizu N, Yang Y, Hayashi Y, Oda M, Fujiwara M, Misawa K and Mori K. 2018. An application of cascaded 3D fully convolutional networks for medical image segmentation. Computerized Medical Imaging and Graphics, 66: 90-99 [DOI: 10.1016/j.compmedimag.2018.03.001]

Samundeeswari P and Gunasundari R. 2020. A novel multilevel hybrid segmentation and refinement method for automatic heterogeneous true NSCLC nodules extraction//Proceedings of the 5th International Conference on Devices, Circuits and Systems (ICDCS). Coimbatore, India: IEEE: 226-235 [ DOI: 10.1109/ICDCS48716.2020.243586 http://dx.doi.org/10.1109/ICDCS48716.2020.243586 ]

Saood A and Hatem I. 2021. COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet. BMC Medical Imaging, 21: #19 [DOI: 10.1186/s12880-020-00529-5]

Sathish R, Sathish R, Sethuraman R and Sheet D. 2020. Lung segmentation and nodule detection in computed tomography scan using a convolutional neural network trained adversarially using turing test loss//2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Montreal, Canada: IEEE: 1331-1334 [ DOI: 10.1109/EMBC44109.2020.9175649 http://dx.doi.org/10.1109/EMBC44109.2020.9175649 ]

Savic M, Ma Y H, Ramponi G, Du W W and Peng Y H. 2021. Lung nodule segmentation with a region-based fast marching method. Sensors, 21(5): 1908 [DOI: 10.3390/s21051908]

Shi H Q, Lu J G and Zhou Q J. 2020a. A novel data augmentation method using style-based GAN for robust pulmonary nodule segmentation//Proceedings of 2020 Chinese Control and Decision Conference (CCDC). Hefei, China: IEEE: 2486-2491 [ DOI: 10.1109/CCDC49329.2020.9164303 http://dx.doi.org/10.1109/CCDC49329.2020.9164303 ]

Shi Z F, Hu Q X, Yue Y H, Wang Z Q, Al-Othmani O M S and Li H L. 2020b. Automatic nodule segmentation method for CT images using aggregation-U-Net generative adversarial networks. Sensing and Imaging, 21: #39 [DOI: 10.1007/s11220-020-00304-4]

Soliman A, Shaffie A, Ghazal M, Gimel'farb G, Keynton R and El-Baz A. 2018. A novel CNN segmentation framework based on using new shape and appearance features//Proceedings of the 25th IEEE International Conference on Image Processing (ICIP). Athens, Greece: IEEE: 3488-3492 [ DOI: 10.1109/ICIP.2018.8451534 http://dx.doi.org/10.1109/ICIP.2018.8451534 ]

Soltani-Nabipour J, Khorshidi A and Noorian B. 2020. Lung tumor segmentation using improved region growing algorithm. Nuclear Engineering and Technology, 52(10): 2313-2319 [DOI: 10.1016/j.net.2020.03.011]

Tan J X, Jing L L, Huo Y M, Li L H, Akin O and Tian Y L. 2021. LGAN: lung segmentation in CT scans using generative adversarial network. Computerized Medical Imaging and Graphics, 87: #101817 [DOI: 10.1016/j.compmedimag.2020.101817]

Vidal P L, de Moura J, Novo J and Ortega M.2021. Multi-stage transfer learning for lung segmentation using portable X-ray devices for patients with COVID-19. Expert Systems with Applications, 173: #114677 [DOI: 10.1016/j.eswa.2021.114677]

Wang X W, Cheng L Y, Huang D Y, Gao X S, Liang D L, He L Y, Zhang Z M, Li N and Tan W J. 2021. Segmentation of pulmonary vessels based on MSFM method//Proceedings of 2020 IEEE International Conference on E-Health Networking, Application and Services (HEALTHCOM). Shenzhen, China: IEEE: 1-4 [ DOI: 10.1109/HEALTHCOM49281.2021.9399043 http://dx.doi.org/10.1109/HEALTHCOM49281.2021.9399043 ]

Wang X Y, Teng P Y, Lo P, Banola A, Kim G, Abtin F, Goldin J and Brown M. 2018. High throughput lung and lobar segmentation by 2D and 3D CNN on chest CT with diffuse lung disease//Image Analysis for Moving Organ, Breast, and Thoracic Images. Granada, Spain: Springer: 202-214 [ DOI: 10.1007/978-3-030-00946-5_21 http://dx.doi.org/10.1007/978-3-030-00946-5_21 ]

Wang Y Q and Yue S H. 2020. Ground glass nodule segmentation based on regional adaptive MRF model//Proceedings of the 39th Chinese Control Conference (CCC). Shenyang, China: IEEE: 6295-6300 [ DOI: 10.23919/CCC50068.2020.9188408 http://dx.doi.org/10.23919/CCC50068.2020.9188408 ]

Wu Y C and Lin L. 2020. Automatic lung segmentation in CT images using dilated convolution based weighted fully convolutional network. Journal of Physics: Conference Series, 1646: #012032 [DOI: 10.1088/1742-6596/1646/1/012032]

Xi Y H, Zhong L M, Xie W J, Qin G G, Liu Y B, Feng Q J and Yang W. 2021. View identification assisted fully convolutional network for lung field segmentation of frontal and lateral chest radiographs. IEEE Access, 9: 59835-59847 [DOI: 10.1109/ACCESS.2021.3074026]

Xia H Y, Sun W F, Song S X and Mou X W. 2020. Md-Net: multi-scale dilated convolution network for CT images segmentation. Neural Processing Letters, 51(3): 2915-2927 [DOI: 10.1007/s11063-020-10230-x]

Xiao H G, Ran Z Q, Huang J F, Ren H J, Liu C, Zhang B L, Zhang B L and Dang J. 2021. Research progress in lung parenchyma segmentation based on computed tomography. Journal of Biomedical Engineering, 38(2): 379-386

肖汉光, 冉智强, 黄金锋, 任慧娇, 刘畅, 张邦林, 张勃龙, 党军. 2021. 基于电子计算机断层扫描图像的肺实质分割方法研究进展. 生物医学工程学杂志, 38(2): 379-386[DOI: 10.7507/1001-5515.202008032]

Xie F, Huang Z, Shi Z J, Wang T Y, Song G L, Wang B L and Liu Z H. 2021. DUDA-Net: a double U-shaped dilated attention network for automatic infection area segmentation in COVID-19 lung CT images. International Journal of Computer Assisted Radiology and Surgery, 16(9): 1425-1434 [DOI: 10.1007/s11548-021-02418-w]

Xu R, Wang Y, Liu T T, Ye X C, Lin L, Chen Y W, Kido S and Tomiyama N. 2021. BG-Net: boundary-guided network for lung segmentation on clinical CT images//Proceedings of the 25th International Conference on Pattern Recognition (ICPR). Milan, Italy: IEEE: 8782-8788 [ DOI: 10.1109/ICPR48806.2021.9412621 http://dx.doi.org/10.1109/ICPR48806.2021.9412621 ]

Yan H L, Lu H J, Ye M C, Yan K, Xu Y G and Jin Q. 2019. Improved mask R-CNN for lung nodule segmentation//Proceedings of the 10th International Conference on Information Technology in Medicine and Education (ITME). Qingdao, China: IEEE: 137-141 [ DOI: 10.1109/ITME.2019.00041 http://dx.doi.org/10.1109/ITME.2019.00041 ]

Zhang J, Yu L D, Chen D C, Pan W D,Shi C, Niu Y, Yao X W, Xu X B and Cheng Y. 2021a. Dense GAN and multi-layer attention based lesion segmentation method for COVID-19 CT images. Biomedical Signal Processing and Control, 69: #102901 [DOI: 10.1016/j.bspc.2021.102901]

Zhang X Q, Wang G Y and Zhao S G. 2021b. COVSeg-NET: a deep convolution neural network for COVID-19 lung CT image segmentation. International Journal of Imaging Systems and Technology, 31(3): 1071-1086 [DOI: 10.1002/ima.22611]

Zhang Z A, Wu C D, Coleman S and Kerr D. 2020. DENSE-INception U-net for medical image segmentation. Computer Methods and Programs in Biomedicine, 192: #105395 [DOI: 10.1016/j.cmpb.2020.105395]

Zhao C, Han J G, Jia Y and Gou F. 2018. Lung nodule detection via 3D U-Net and contextual convolutional neural network//Proceedings of 2018 International Conference on Networking and Network Applications (NaNA). Xi'an, China: IEEE: 356-361 [ DOI: 10.1109/NANA.2018.8648753 http://dx.doi.org/10.1109/NANA.2018.8648753 ]

Zhao S X, Li Z D, Chen Y, Zhao W, Xie X Z, Liu J, Zhao D and Li Y J. 2021a. SCOAT-Net: a novel network for segmenting COVID-19 lung opacification from CT images. Pattern Recognition, 119: #108109 [DOI: 10.1016/j.patcog.2021.108109]

Zhao Y Q, Yu X Y, Wu H B, Zhou Y, Sun X M, Yu S, Yu S C and Liu H. 2021b. A fast 2-D Otsu lung tissue image segmentation algorithm based on improved PSO. Microprocessors and Microsystems, 80: #103527 [DOI: 10.1016/j.micpro.2020.103527]

Zheng B B, Liu Y Q, Zhu Y, Yu F L, Jiang T J, Yang D W and Xu T. 2020. MSD-Net: multi-scale discriminative network for COVID-19 lung infection segmentation on CT. IEEE Access, 8: 185786-185795 [DOI: 10.1109/ACCESS.2020.3027738]

Zhong Z S, Kim Y, Zhou L X, Plichta K, Allen B, Buatti J and Wu X D. 2018. 3D fully convolutional networks for co-segmentation of tumors on PET-CT images//Proceedings of the 15th IEEE International Symposium on Biomedical Imaging (ISBI 2018). Washington, USA: IEEE: 228-231 [ DOI: 10.1109/ISBI.2018.8363561 http://dx.doi.org/10.1109/ISBI.2018.8363561 ]

Zhou X R, Takayama R, Wang S, Hara T and Fujita H. 2017. Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method. Medical Physics, 44(10): 5221-5233 [DOI: 10.1002/mp.12480]