Research on Segmentation Technology in Lung Cancer Radiotherapy
Based on Deep Learning

Jun      Huang; Tao      Liu; Beibei      Qian; Zhibo      Chen; Ya      Wang
doi:10.2174/1573405619666230123104243
Abstract

Background: Lung cancer has the highest mortality rate among cancers. Radiation therapy (RT) is one of the most effective therapies for lung cancer. The correct segmentation of lung tumors (LTs) and organs at risk (OARs) is the cornerstone of successful RT.
Methods: We searched four databases for relevant material published in the last 10 years: Web of Science, PubMed, Science Direct, and Google Scholar. The advancement of deep learning-based segmentation technology for lung cancer radiotherapy (DSLC) research was examined from the perspectives of LTs and OARs.
Results: In this paper, Most of the dice similarity coefficient (DSC) values of LT segmentation in the surveyed literature were above 0.7, whereas the DSC indicators of OAR segmentation were all over 0.8.
Conclusion: The contribution of this review is to summarize DSLC research methods and the issues that DSLC faces are discussed, as well as possible viable solutions. The purpose of this review is to encourage collaboration among experts in lung cancer radiotherapy and DL and to promote more research into the use of DL in lung cancer radiotherapy.
Graphical Abstract

[1]
Bray F, Laversanne M, Weiderpass E, Soerjomataram I. The ever increasing importance of cancer as a leading cause of premature death worldwide. Cancer  2021; 127(16): 3029-30.
 [http://dx.doi.org/10.1002/cncr.33587] [PMID:  34086348]

[2]
Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin  2021; 71(3): 209-49.
 [http://dx.doi.org/10.3322/caac.21660] [PMID:  33538338]

[3]
Latest global cancer data: Cancer burden rises to 19.3 million new cases and 10.0 million cancer deaths in 2020. 2020. Available from:  https://web.archive.org/web/202 20301120144/ https://www.iarc.who.int/wp-content/upl oads/2020/12/pr292_E.pdf(Accessedon: 12 february 2022).

[4]
Vinod SK, Hau E. Radiotherapy treatment for lung cancer: Current status and future directions. Respirology  2020; 25 (Suppl. 2): 61-71.
 [http://dx.doi.org/10.1111/resp.13870] [PMID:  32516852]

[5]
Nagata Y, Kimura T. Stereotactic body radiotherapy (SBRT) for Stage I lung cancer. Jpn J Clin Oncol  2018; 48(5): 405-9.
 [http://dx.doi.org/10.1093/jjco/hyy034] [PMID:  29635536]

[6]
Brown S, Banfill K, Aznar MC, Whitehurst P, Faivre Finn C. The evolving role of radiotherapy in non-small cell lung cancer. Br J Radiol  2019; 92(1104): 20190524.
 [http://dx.doi.org/10.1259/bjr.20190524] [PMID:  31535580]

[7]
Burdett S, Rydzewska L, Tierney J, et al. Postoperative radiotherapy for non small cell lung cancer. Cochrane Database  System Rev  2016; 10(10): CD002142.
 [http://dx.doi.org/10.1002/14651858.CD002142.pub3]

[8]
Baker S, Dahele M, Lagerwaard FJ, Senan S. A critical review of recent developments in radiotherapy for non-small cell lung cancer. Radiat Oncol  2016; 11(1): 115.
 [http://dx.doi.org/10.1186/s13014-016-0693-8] [PMID:  27600665]

[9]
Meyer P, Noblet V, Mazzara C, Lallement A. Survey on deep learning for radiotherapy. Comput Biol Med  2018; 98: 126-46.
 [http://dx.doi.org/10.1016/j.compbiomed.2018.05.018] [PMID:  29787940]

[10]
Samarasinghe G, Jameson M, Vinod S, et al. Deep learning for segmentation in radiation therapy planning: a review. J Med Imaging Radiat Oncol  2021; 65(5): 578-95.
 [http://dx.doi.org/10.1111/1754-9485.13286] [PMID:  34313006]

[11]
Daisne JF, Blumhofer A. Atlas-based automatic segmentation of head and neck organs at risk and nodal target volumes: a clinical validation. Radiat Oncol  2013; 8(1): 154.
 [http://dx.doi.org/10.1186/1748-717X-8-154] [PMID:  23803232]

[12]
Cabezas M, Oliver A, Lladó X, Freixenet J, Bach Cuadra M. A review of atlas-based segmentation for magnetic resonance brain images. Comput Methods Programs Biomed  2011; 104(3): e158-77.
 [http://dx.doi.org/10.1016/j.cmpb.2011.07.015] [PMID:  21871688]

[13]
Bai W, Shi W, Ledig C, Rueckert D. Multi-atlas segmentation with augmented features for cardiac MR images. Med Image Anal  2015; 19(1): 98-109.
 [http://dx.doi.org/10.1016/j.media.2014.09.005] [PMID:  25299433]

[14]
Meiburger KM, Acharya UR, Molinari F. Automated localization and segmentation techniques for B-mode ultrasound images: A review. Comput Biol Med  2018; 92: 210-35.
 [http://dx.doi.org/10.1016/j.compbiomed.2017.11.018] [PMID:  29247890]

[15]
Wang Y, Zhao L, Wang M, Song Z. Organ at risk segmentation in head and neck ct images using a two-stage segmentation framework based on 3D U-Net IEEE Access   2019; 7: 144591-602.

[16]
Liu C, Gardner SJ, Wen N, et al. Automatic segmentation of the prostate on CT images using deep neural networks (DNN). Int J Radiat Oncol Biol Phys  2019; 104(4): 924-32.

[17]
Men K, Zhang T, Chen X, et al. Fully automatic and robust segmentation of the clinical target volume for radiotherapy of breast cancer using big data and deep learning. Phys Med  2018; 50: 13-9.
 [http://dx.doi.org/10.1016/j.ejmp.2018.05.006] [PMID:  29891089]

[18]
Avanzo M, Stancanello J, Pirrone G, Sartor G. Radiomics and deep learning in lung cancer. Strahlenther Onkol  2020; 196(10): 879-87.
 [http://dx.doi.org/10.1007/s00066-020-01625-9] [PMID:  32367456]

[19]
Liu Z, Yao C, Yu H, Wu T. Deep reinforcement learning with its application for lung cancer detection in medical Internet of Things. Future Gener Comput Syst  2019; 97: 1-9.
 [http://dx.doi.org/10.1016/j.future.2019.02.068]

[20]
Polat H, Danaei Mehr H. Classification of pulmonary CT images by using hybrid 3D-deep convolutional neural network architecture. Appl Sci (Basel)  2019; 9(5): 940.
 [http://dx.doi.org/10.3390/app9050940]

[21]
Vrtovec T, Močnik D, Strojan P, Pernuš F, Ibragimov B. Auto segmentation of organs at risk for head and neck radiotherapy planning: From atlas based to deep learning methods. Med Phys  2020; 47(9): e929-50.
 [http://dx.doi.org/10.1002/mp.14320] [PMID:  32510603]

[22]
Kholiavchenko M, Sirazitdinov I, Kubrak K, et al. Contour-aware multi-label chest X-ray organ segmentation. Int J CARS  2020; 15(3): 425-36.
 [http://dx.doi.org/10.1007/s11548-019-02115-9] [PMID:  32034633]

[23]
Tamang LD, Kim BW. Deep learning approaches to colorectal cancer diagnosis: A review. Appl Sci (Basel)  2021; 11(22): 10982.
 [http://dx.doi.org/10.3390/app112210982]

[24]
Cao H, Liu H, Song E, et al. A two-stage convolutional neural networks for lung nodule detection. IEEE J Biomed Health Inform  2020; 24(7): 1.
 [http://dx.doi.org/10.1109/JBHI.2019.2963720] [PMID:  31905154]

[25]
Wong J, Fong A, McVicar N, et al. Comparing deep learning-based auto-segmentation of organs at risk and clinical target volumes to expert inter-observer variability in radiotherapy planning. Radiother Oncol  2020; 144: 152-8.
 [http://dx.doi.org/10.1016/j.radonc.2019.10.019] [PMID:  31812930]

[26]
Men K, Dai J, Li Y. Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks. Med Phys  2017; 44(12): 6377-89.
 [http://dx.doi.org/10.1002/mp.12602] [PMID:  28963779]

[27]
Souza JC, Bandeira Diniz JO, Ferreira JL, França da Silva GL, Corrêa Silva A, de Paiva AC. An automatic method for lung segmentation and reconstruction in chest X-ray using deep neural networks. Comput Methods Programs Biomed  2019; 177: 285-96.
 [http://dx.doi.org/10.1016/j.cmpb.2019.06.005] [PMID:  31319957]

[28]
Shaziya H, Shyamala K, Zaheer R. Automatic lung segmentation on thoracic CT scans using U-net convolutional network.2018  International conference on communication and signal processing (ICCSP) 3-5 April 2018; Chennai, India: IEEE;  0643-7.
 [http://dx.doi.org/10.1109/ICCSP.2018.8524484]

[29]
Wang C, Tyagi N, Rimner A, et al. Segmenting lung tumors on longitudinal imaging studies via a patient-specific adaptive convolutional neural network. Radiother Oncol  2019; 131: 101-7.
 [http://dx.doi.org/10.1016/j.radonc.2018.10.037] [PMID:  30773175]

[30]
Han M, Yao G, Zhang W, et al. Segmentation of CT thoracic organs by multi-resolution VB-nets. >SegTHOR@ ISBI; 8-11 April  Venice, Italy 2019.

[31]
Park J, Yun J, Kim N, et al. Fully automated lung lobe segmentation in volumetric chest CT with 3D U-Net: validation with intra-and extra-datasets. J Digit Imaging  2020; 33(1): 221-30.
 [http://dx.doi.org/10.1007/s10278-019-00223-1] [PMID:  31152273]

[32]
Hupe M. EndNote X9. J Electron Resour Med Libr  2019; 16(3-4): 117-9.
 [http://dx.doi.org/10.1080/15424065.2019.1691963]

[33]
Fu Y, Lei Y, Wang T, Curran WJ, Liu T, Yang X. A review of deep learning based methods for medical image multi-organ segmentation. Phys Med  2021; 85: 107-22.
 [http://dx.doi.org/10.1016/j.ejmp.2021.05.003] [PMID:  33992856]

[34]
Sharif MI, Li JP, Naz J, Rashid I. A comprehensive review on multi-organs tumor detection based on machine learning. Pattern Recognit Lett  2020; 131: 30-7.
 [http://dx.doi.org/10.1016/j.patrec.2019.12.006]

[35]
Wang S, Yang DM, Rong R, et al. Artificial intelligence in lung cancer pathology image analysis. Cancers (Basel)  2019; 11(11): 1673.
 [http://dx.doi.org/10.3390/cancers11111673] [PMID:  31661863]

[36]
Zhang G, Jiang S, Yang Z, et al. Automatic nodule detection for lung cancer in CT images: A review. Comput Biol Med  2018; 103: 287-300.
 [http://dx.doi.org/10.1016/j.compbiomed.2018.10.033] [PMID:  30415174]

[37]
Liu X, Li KW, Yang R, Geng LS. Review of deep learning based automatic segmentation for lung cancer radiotherapy. Front Oncol  2021; 2021: 11717039.
 [http://dx.doi.org/10.3389/fonc.2021.717039] [PMID:  34336704]

[38]
Kao YS, Yang J. Deep learning-based auto-segmentation of lung tumor PET/CT scans: a systematic review. Clin Transl Imaging  2022; 10(2): 217-23.
 [http://dx.doi.org/10.1007/s40336-022-00482-z]

[39]
Smith K, Nolan T. NSCLC Radiogenomics  Available from:https://web.archive.org/web/20220301 091241/ https://wiki.cancerimagingarchive.net/display/Public/NSCLC+Radiogenomics (Accessed on: 11 February 2022).

[40]
Vendt B, Nolan T. The Lung Image Database Consortium image collection  Available from:https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI (Accessed on: 3 January 2022).

[41]
Nolan T, Jarosz Q. Lung CT segmentation challenge 2017. Available from:ive.org/web/20220301131137/ https://wiki.cancerimagingarchive.net/display/Public/Lung+CT+Segmentation+Challenge+2017(Accessed on: 18 February 2022).

[42]
DeepLesion  Available from:ive.org/web/20220301131812/ https://nihcc.app.box.com/v/DeepLesion (Accessed on: 9 February 2022).

[43]
NLST Datasets.  Available from: rchive.org/web/20220301140337/ https://cdas.cancer.gov/datasets/nlst/ (Accessed on: 5 February 2022).

[44]
Data Science Bowl 2017. Available from:  https://www.kaggle.com/c/data-science-bowl-2017/(Accessed on: 30 December 2021).

[45]
NIH Chest X-rays 2022. Available from:https://www.kaggle.com/nih-chest-xrays/data/ (Accessed on: 12 February 2022).

[46]
Smith K, Nolan T, QIN Lung CT.  Available from: https://wiki.cancerimagingarchive.net/display/Public/QIN+LUNG+CT/ (Accessed on: 15 february 2022).

[47]
Lung Nodule Analysis 2016. Available from:https://web.archive.org/web/20220301142254/ https://luna16.grand-challenge.org/Data/(Accessed on: 8 February 2022).

[48]
Kirby J, Jarosz Q. SPIE-AAPM Lung CT Challenge  Available from:https://web.archive.org/w eb/20220301142410/ https://wiki.cancerimagingarchive.net/display/Public/SPIE-AAPM+Lung+CT+Challenge(Accessed on: 12 January 2022).

[49]
Clark K, Jarosz Q. LungCT-Diagnosis  Available from: https://web.archive.org/web/202 2030 1143334/ https://wiki.cancerimagingarchive.net/display/Public/LungCT-Diagnosis (Accessed on: 13 February 2022).

[50]
Web Archive The cancer imaging archive  Available from: https://web.archive.org/web/20220302020242/ https://www.cancerimagingarchive.net/(Accessed on: 13 February 2022).

[51]
TCGA. The Cancer Genome Atlas Program  Available from:https://web.archive.org/web/2022 0301143553/ https://www.cancer.gov/about-nci/organiz ation/ccg/research/structural-genomics/tcga (Accessed on: 11 February 2022).

[52]
ImageCLEF/LifeCLEF-Multimedia Retrieval in CLEF  Available from:https://web.archive.org/web/20220301143736/ https://www.imageclef.org/2017/tuberculosis(Accessed on: 3 January 2022).

[53]
 SCR database: Segmentation in chest radiographs  Available from:https://web.archive.org/web/20220302073253/ https://www.isi.uu.nl/Research/Databases/SCR/ (Accessed on: 10 January 2022).

[54]
JSRT. Database.  Available from: rchive.org/web/20220302073541/ http://db.jsrt.or.jp/eng.php (Accessed on: 10 January 2022).

[55]
Armato SG III, McLennan G, Bidaut L, et al. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans. Med Phys  2011; 38(2): 915-31.
 [http://dx.doi.org/10.1118/1.3528204] [PMID:  21452728]

[56]
Setio AAA, Traverso A, de Bel T, et al. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge. Med Image Anal  2017; 42: 1-13.
 [http://dx.doi.org/10.1016/j.media.2017.06.015] [PMID:  28732268]

[57]
Sahiner B, Pezeshk A, Hadjiiski LM, et al. Deep learning in medical imaging and radiation therapy. Med Phys  2019; 46(1): e1-e36.
 [http://dx.doi.org/10.1002/mp.13264] [PMID:  30367497]

[58]
Zhou X, Li C, Rahaman MM, et al. A comprehensive review for breast histopathology image analysis using classical and deep neural networks IEEE Access   2020; 8: 90931-56.
 [http://dx.doi.org/10.1109/ACCESS.2020.2993788]

[59]
Lee LK, Liew SC, Thong WJ. A review of image segmentation methodologies in medical image. Adv Comput Commun Eng Technol  2015; 315(1069): 80.
 [http://dx.doi.org/10.1007/978-3-319-07674-4_99]

[60]
LeCun Y, Kavukcuoglu K, Farabet C. Convolutional networks and applications in vision. Proceedings of 2010 IEEE international symposium on circuits and systems  30 May-2 June 2010; Paris, France: IEEE. 253-6.

[61]
Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets. Adv Neural Inf Process Syst  2014; 2014: 27.

[62]
Raza K, Singh NK. A tour of unsupervised deep learning for medical image analysis. Curr Med Imaging Rev  2021; 17(9): 1059-77.
 [http://dx.doi.org/10.2174/1573405617666210127154257] [PMID:  33504314]

[63]
Simonyan K, Zisserman A. Very deep convolutional networks forlarge-scale image recognition arXiv 2014;2014  14091556 

[64]
Zhao T, Gao D, Wang J, Yin Z. Lung segmentation in CT images using a fully convolutional neural network with multi-instance and conditional adversary loss. 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) 24 May 2018; Washington DC, USA: IEEE; .  505-9.
 [http://dx.doi.org/10.1109/ISBI.2018.8363626]

[65]
Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition.  Boston, MA, USA. 2015; pp. 3431-40.

[66]
Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation In: Navab  N,, Hornegger j, Wells W, Frangi A, Eds. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015: MICCAI 2015 Lecture Notes in Computer Science, Vol9351 Springer, Cham. 
 [http://dx.doi.org/10.1007/978-3-319-24574-4_28]

[67]
Liu T, Qian B, Wang Y, Xie Q. U-Net medical image segmentation based on attention mechanism combination. Int Conf Cogn Inform Proc Appl (CIPA)  2021; 2021: 805-13.
 [http://dx.doi.org/10.1007/978-981-16-5857-0_103]

[68]
Zou KH, Warfield SK, Bharatha A, et al. Statistical validation of image segmentation quality based on a spatial overlap index1. Acad Radiol  2004; 11(2): 178-89.
 [http://dx.doi.org/10.1016/S1076-6332(03)00671-8] [PMID:  14974593]

[69]
Anwar SM, Majid M, Qayyum A, Awais M, Alnowami M, Khan MK. Medical image analysis using convolutional neural networks: a review. J Med Syst  2018; 42(11): 226.
 [http://dx.doi.org/10.1007/s10916-018-1088-1] [PMID:  30298337]

[70]
Havaei M, Davy A, Warde-Farley D, et al. Brain tumor segmentation with deep neural networks. Med Image Anal  2017; 35: 18-31.
 [http://dx.doi.org/10.1016/j.media.2016.05.004] [PMID:  27310171]

[71]
Yuan Y, Chao M, Lo YC. Automatic skin lesion segmentation using deep fully convolutional networks with jaccard distance. IEEE Trans Med Imaging  2017; 36(9): 1876-86.
 [http://dx.doi.org/10.1109/TMI.2017.2695227] [PMID:  28436853]

[72]
Kumar Y, Gupta S, Singla R, Hu Y-C. A systematic review of artificial intelligence techniques in cancer prediction and diagnosis. Arch Comput Methods Eng  2021; 2021: 1-28.
 [PMID:  34602811]

[73]
Feng X, Qing K, Tustison NJ, Meyer CH, Chen Q. Deep convolutional neural network for segmentation of thoracic organs‐at‐risk using cropped 3D images. Med Phys  2019; 46(5): 2169-80.
 [http://dx.doi.org/10.1002/mp.13466] [PMID:  30830685]

[74]
Zhao X, Li L, Lu W, Tan S. Tumor co-segmentation in PET/CT using multi-modality fully convolutional neural network. Phys Med Biol  2018; 64(1): 015011.
 [http://dx.doi.org/10.1088/1361-6560/aaf44b] [PMID:  30523964]

[75]
Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA Cancer J Clin  2022; 72(1): 7-33.
 [http://dx.doi.org/10.3322/caac.21708] [PMID:  35020204]

[76]
Sheng K. Artificial intelligence in radiotherapy: a technological review. Front Med  2020; 14(4): 431-49.
 [http://dx.doi.org/10.1007/s11684-020-0761-1] [PMID:  32728877]

[77]
Zhang F, Wang Q, Li H. Automatic segmentation of the gross target volume in non-small cell lung cancer using a modified version of resNet. Technol Cancer Res Treat  2020; 19: 1533033820947484.
 [http://dx.doi.org/10.1177/1533033820947484]

[78]
Pang S, Du A, Orgun MA, et al. CTumorGAN: a unified framework for automatic computed tomography tumor segmentation. Eur J Nucl Med Mol Imaging  2020; 47(10): 2248-68.
 [http://dx.doi.org/10.1007/s00259-020-04781-3] [PMID:  32222809]

[79]
Jiang J, Hu YC, Tyagi N, et al. Cross modality (CTMRI) prior augmented deep learning for robust lung tumor segmentation from small MR datasets. Med Phys  2019; 46(10): 4392-404.
 [http://dx.doi.org/10.1002/mp.13695] [PMID:  31274206]

[80]
Jiang J, Riyahi Alam S, Chen I, et al. Deep cross modality (MR CT) educed distillation learning for cone beam CT lung tumor segmentation. Med Phys  2021; 48(7): 3702-13.
 [http://dx.doi.org/10.1002/mp.14902] [PMID:  33905558]

[81]
Leung KH, Marashdeh W, Wray R, et al. A physics-guided modular deep-learning based automated framework for tumor segmentation in PET. Phys Med Biol  2020; 65(24): 245032.
 [http://dx.doi.org/10.1088/1361-6560/ab8535] [PMID:  32235059]

[82]
Li L, Zhao X, Lu W, Tan S. Deep learning for variational multimodality tumor segmentation in PET/CT. Neurocomputing  2020; 392: 277-95.
 [http://dx.doi.org/10.1016/j.neucom.2018.10.099] [PMID:  32773965]

[83]
Bi L, Fulham M, Li N, et al. Recurrent feature fusion learning for multi-modality pet-ct tumor segmentation. Comput Methods Programs Biomed  2021; 2021: 203106043.
 [http://dx.doi.org/10.1016/j.cmpb.2021.106043] [PMID:  33744750]

[84]
Fu X, Bi L, Kumar A, Fulham M, Kim J. Multimodal spatial attention module for targeting multimodal PET-CT lung tumor segmentation. IEEE J Biomed Health Inform  2021; 25(9): 3507-16.
 [http://dx.doi.org/10.1109/JBHI.2021.3059453] [PMID:  33591922]

[85]
Bi N, Wang J, Zhang T, et al. Deep learning improved clinical target volume contouring quality and efficiency for postoperative radiation therapy in non-small cell lung cancer. Front Oncol  2019; 9: 1192.
 [http://dx.doi.org/10.3389/fonc.2019.01192] [PMID:  31799181]

[86]
Jemaa S, Fredrickson J, Carano RAD, Nielsen T, de Crespigny A, Bengtsson T. Tumor segmentation and feature extraction from whole-body FDG-PET/CT using cascaded 2D and 3D convolutional neural networks. J Digit Imaging  2020; 33(4): 888-94.
 [http://dx.doi.org/10.1007/s10278-020-00341-1] [PMID:  32378059]

[87]
Jiang J, Hu YC, Liu CJ, et al. Multiple resolution residually connected feature streams for automatic lung tumor segmentation from CT images. IEEE Trans Med Imaging  2019; 38(1): 134-44.
 [http://dx.doi.org/10.1109/TMI.2018.2857800] [PMID:  30040632]

[88]
Vallières M, Freeman CR, Skamene SR, El Naqa I. A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol  2015; 60(14): 5471-96.
 [http://dx.doi.org/10.1088/0031-9155/60/14/5471] [PMID:  26119045]

[89]
Zhu J, Zhang J, Qiu B, Liu Y, Liu X, Chen L. Comparison of the automatic segmentation of multiple organs at risk in CT images of lung cancer between deep convolutional neural network-based and atlas-based techniques. Acta Oncol  2019; 58(2): 257-64.
 [http://dx.doi.org/10.1080/0284186X.2018.1529421] [PMID:  30398090]

[90]
Vesal S, Ravikumar N, Maier A. A 2D dilated residual U-Net for multi-organ segmentation in thoracic CT. arXiv  2019; 2019: 190507710.

[91]
Dong X, Lei Y, Wang T, et al. Automatic multiorgan segmentation in thorax CT images using U‐net‐GAN. Med Phys  2019; 46(5): 2157-68.
 [http://dx.doi.org/10.1002/mp.13458] [PMID:  30810231]

[92]
He T, Hu J, Song Y, Guo J, Yi Z. Multi-task learning for the segmentation of organs at risk with label dependence. Med Image Anal  2020; 61101666.
 [http://dx.doi.org/10.1016/j.media.2020.101666] [PMID:  32062155]

[93]
Chen S, Zhong X, Hu S, et al. Automatic multiorgan  segmentation in dual energy CT (DECT) with dedicated 3D fully convolutional DECT networks. Med Phys  2020; 47(2): 552-62.
 [http://dx.doi.org/10.1002/mp.13950] [PMID:  31816095]

[94]
Hu Q, de F, Souza LF, Holanda GB, et al. An effective approach for CT lung segmentation using mask region-based convolutional neural networks. Artif Intell Med  2020; 2020: 103101792.
 [http://dx.doi.org/10.1016/j.artmed.2020.101792] [PMID:  32143797]

[95]
van Harten LD, Noothout JM, Verhoeff JJ, Wolterink JM, Isgum I. Automatic segmentation of organs at risk in thoracic CT scans by combining 2D and 3D convolutional neural networks.SegTHOR@ ISBI 2019;2019 139099960. 

[96]
Akila Agnes S, Anitha J, Dinesh Peter J. Automatic lung segmentation in low-dose chest CT scans using convolutional deep and wide network (CDWN). Neural Comput Appl  2020; 32(20): 15845-55.
 [http://dx.doi.org/10.1007/s00521-018-3877-3]

[97]
Zhang T, Yang Y, Wang J, et al. Comparison between atlas and convolutional neural network based automatic segmentation of multiple organs at risk in non-small cell lung cancer. Medicine (Baltimore)  2020; 99(34): e21800.
 [http://dx.doi.org/10.1097/MD.0000000000021800] [PMID:  32846816]

[98]
Cid YD, Del Toro OAJ, Depeursinge A, Müller H. Efficient and fully automatic segmentation of the lungs in CT volumes. VISCERAL Challenge@ ISBI 16-19 April 2015; NY, USA.  31-5.

[99]
Lambert Z, Petitjean C, Dubray B, Kuan S. Tenth International Conference on Image Processing Theory, Tools and Applications (IPTA) 9-12 November   2020; Paris, France: IEEE. 2020; pp. 1-6.

[100]
Choi RY, Coyner AS, Kalpathy-Cramer J, Chiang MF, Campbell JP. Introduction to machine learning, neural networks, and deep learning. Transl Vis Sci Technol  2020; 9(2): 14-4.
 [PMID:  32704420]

[101]
Winfield JM, Payne GS, deSouza NM. Functional MRI and CT biomarkers in oncology. Eur J Nucl Med Mol Imaging  2015; 42(4): 562-78.
 [http://dx.doi.org/10.1007/s00259-014-2979-0] [PMID:  25578953]

[102]
Fechter T, Adebahr S, Baltas D, Ben Ayed I, Desrosiers C, Dolz J. Esophagus segmentation in CT via 3D fully convolutional neural network and random walk. Med Phys  2017; 44(12): 6341-52.
 [http://dx.doi.org/10.1002/mp.12593] [PMID:  28940372]

[103]
Giovannini S, Macchi C, Liperoti R, et al. Association of body fat with health-related quality of life and depression in nonagenarians: The mugello study. J Am Med Dir Assoc  2019; 20(5): 564-8.
 [http://dx.doi.org/10.1016/j.jamda.2019.01.128] [PMID:  30852165]

[104]
Lin X, Jiao H, Pang Z, et al. Lung cancer and granuloma identification using a deep learning model to extract 3-dimensional radiomics features in CT imaging. Clin Lung Cancer  2021; 22(5): e756-66.
 [http://dx.doi.org/10.1016/j.cllc.2021.02.004] [PMID:  33678583]

[105]
Coraci D, Giovannini S, Loreti C, Fusco A, Padua L. Management of neuropathic pain: A graph theory based presentation of literature review. Breast J  2020; 26(3): 581-2.
 [http://dx.doi.org/10.1111/tbj.13622] [PMID:  31495044]
Rights & Permissions Print Cite
Journal Information
For Authors
For Editors
For Reviewers
Explore Articles
For Visitors
DOI https://dx.doi.org/10.2174/1573405619666230123104243	Print ISSN 1573-4056
Publisher Name Bentham Science Publisher	Online ISSN 1875-6603
Current Medical Imaging

Research on Segmentation Technology in Lung Cancer Radiotherapy Based on Deep Learning

Abstract Play Pause

Graphical Abstract

Related Books

Abstract
