PDF herunterladen
CC BY-NC-ND 4.0 · Yearb Med Inform 2022; 31(01): 277-295
DOI: 10.1055/s-0042-1742517
Section 12: Sensor, Signal and Imaging Informatics
Survey

A Systematic Review of Automated Segmentation Methods and Public Datasets for the Lung and its Lobes and Findings on Computed Tomography Images

Diedre Carmo‡   *
1   School of Electrical and Computer Engineering, University of Campinas, Brazil
,
Jean Ribeiro‡   *
1   School of Electrical and Computer Engineering, University of Campinas, Brazil
,
Sergio Dertkigil
2   School of Medical Sciences, University of Campinas, Brazil
,
Simone Appenzeller
2   School of Medical Sciences, University of Campinas, Brazil
,
Roberto Lotufo
1   School of Electrical and Computer Engineering, University of Campinas, Brazil
,
Leticia Rittner
1   School of Electrical and Computer Engineering, University of Campinas, Brazil
› Institutsangaben
› Weitere Informationen
Auch verfügbar auf
   Lizenzen und Reprints

Summary

Objectives: Automated computational segmentation of the lung and its lobes and findings in X-Ray based computed tomography (CT) images is a challenging problem with important applications, including medical research, surgical planning, and diagnostic decision support. With the increase in large imaging cohorts and the need for fast and robust evaluation of normal and abnormal lungs and their lobes, several authors have proposed automated methods for lung assessment on CT images. In this paper we intend to provide a comprehensive summarization of these methods.

Methods: We used a systematic approach to perform an extensive review of automated lung segmentation methods. We chose Scopus, PubMed, and Scopus to conduct our review and included methods that perform segmentation of the lung parenchyma, lobes or internal disease related findings. The review was not limited by date, but rather by only including methods providing quantitative evaluation.

Results: We organized and classified all 234 included articles into various categories according to methodological similarities among them. We provide summarizations of quantitative evaluations, public datasets, evaluation metrics, and overall statistics indicating recent research directions of the field.

Conclusions: We noted the rise of data-driven models in the last decade, especially due to the deep learning trend, increasing the demand for high-quality data annotation. This has instigated an increase of semi-supervised and uncertainty guided works that try to be less dependent on human annotation. In addition, the question of how to evaluate the robustness of data-driven methods remains open, given that evaluations derived from specific datasets are not general.

Keywords

Lung - lung diseases - automated pattern recognition - x-ray computed tomography - medical image segmentation

* Equal contribution


Supplementary Material



Publikationsverlauf

Artikel online veröffentlicht:
04. Dezember 2022

© 2022. IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Buzug TM. Computed tomography. In: Springer handbook of medical technology. Springer; 2011. p. 311–42.
    PubMed
  • 2 Mansoor A, Bagci U, Foster B, Xu Z, Papadakis GZ, Folio LR, et al. Segmentation and Image Analysis of Abnormal Lungs at CT: Current Approaches, Challenges, and Future Trends. Radiographics 2015 Jul-Aug;35(4):1056-76.
    PubMed
  • 3 Tabatabaei SMH, Talari H, Moghaddas F, Rajebi H. CT Features and Short-term Prognosis of COVID-19 Pneumonia: A Single-Center Study from Kashan, Iran. Radiol Cardiothorac Imaging 2020 Apr 20;2(2):e200130.
    PubMed
  • 4 Hofmanninger J, Prayer F, Pan J, Röhrich S, Prosch H, Langs G. Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem. Eur Radiol Exp 2020 Aug 20;4(1):50.
    PubMed
  • 5 Kim SS, Seo JB, Lee HY, Nevrekar DV, Forssen AV, Crapo JD, et al. Chronic obstructive pulmonary disease: lobe-based visual assessment of volumetric CT by Using standard images--comparison with quantitative CT and pulmonary function test in the COPDGene study. Radiology 2013 Feb;266(2):626-35.
    PubMed
  • 6 Doel T, Gavaghan DJ, Grau V. Review of automatic pulmonary lobe segmentation methods from CT. Comput Med Imaging Graph 2015 Mar;40:13-29.
    PubMed
  • 7 Roberts M, Driggs D, Thorpe M, Gilbey J, Yeung M, Ursprung S, et al. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nat Mach Intell 2021;3(3):199-217.
    PubMed
  • 8 Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, et al. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ 2020 Apr 7;369:m1328. Update in: BMJ 2021 Feb 3;372:n236. Erratum in: BMJ 2020 Jun 3;369:m2204.
    PubMed
  • 9 Biolchini J, Mian PG, Natali ACC, Travassos GH. Systematic review in software engineering. System engineering and computer science department COPPE/UFRJ. Technical Report ES 2005;679(05):45.
    PubMed
  • 10 Boland A, Cherry G, Dickson R, editors. Doing a systematic review: A student’s guide. SAGE Publications; 2017.
    PubMed
  • 11 Babineau J. Product review: Covidence (systematic review software). J Can Health Libr Assoc 2014, 35(2):68-71.
    PubMed
  • 12 Tseng LY, Huang LC. An adaptive thresholding method for automatic lung segmentation in CT images. In: AFRICON2009. IEEE; 2009. p. 1-5.
    PubMed
  • 13 Birkbeck N, Kohlberger T, Zhang J, Sofka M, Kaftan J, Comaniciu D, et al. Lung segmentation from CT with severe pathologies using anatomical constraints. In: International conference on Medical Image Computing and Computer-Assisted Intervention—MICCAI 2014. Cham: Springer; 2012. p. 804-11.
    PubMed
  • 14 Mohd Noor, N, Mohd Rijal, O, Ming JTC, Rosell FA, Ebrahimian H, Kassim RM, et aä. Segmentation of the lung anatomy for high resolution computed tomography (HRCT) thorax images. In: International Visual Informatics Conference 2013. Cham: Springer; 2013. p. 165-75.
    PubMed
  • 15 Reboucas Filho PP, da Silva Barros AC, Almeida JS, Rodrigues JPC, de Albuquerque VHC. A new effective and powerful medical image segmentation algorithm based on optimum path snakes. Appl Soft Comput 2019;76:649-70.
    PubMed
  • 16 Mansoor A, Casas Jr R, Linguraru MG. Spatial context learning approach to automatic segmentation of pleural effusion in chest computed tomography images. In: Medical Imaging 2016: Computer-Aided Diagnosis, Vol. 9785. International Society for Optics and Photonics; 2016. p. 978514 .
    PubMed
  • 17 Somasundaram E, Kaufman R, Brady S. Advancements in automated tissue segmentation pipeline for contrast-enhanced CT scans of adult and pediatric patients. In: Medical Imaging 2017: Computer-Aided Diagnosis, Vol. 10134. International Society for Optics and Photonics; 2017. p. 101343.
    PubMed
  • 18 Zhang X, Li S, Zhang B, Dong J, Zhao S, Liu X. Automatic detection and segmentation of lung nodules in different locations from CT images based on adaptivea-hull algorithm and DenseNet convolutional network. Int J Imaging Syst Technol 2021;31(4):1882-93.
    PubMed
  • 19 Kohlberger T, Sofka M, Zhang J, Birkbeck N, Wetzl J, Kaftan J, et al. Automatic multi-organ segmentation using learning-based segmentation and level set optimization. In: International Conference on Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011. Springer; 2011. p. 338-45.
    PubMed
  • 20 Wei Y, Shen G, Li JJ. A fully automatic method for lung parenchyma segmentation and repairing. J Digit Imaging. 2013 Jun;26(3):483-95.
    PubMed
  • 21 Ross JC, Kindlmann GL, Okajima Y, Hatabu H, Díaz AA, Silverman EK, et al. Pulmonary lobe segmentation based on ridge surface sampling and shape model fitting. Med Phys 2013 Dec;40(12):121903.
    PubMed
  • 22 Wei Q, Hu Y. A hybrid approach to segmentation of diseased lung lobes. IEEE J Biomed Health Inform 2014 Sep;18(5):1696-706.
    PubMed
  • 23 ill G, Beichel RR. An approach for reducing the error rate in automated lung segmentation. Comput Biol Med 2016 Sep 1;76:143-53.
    PubMed
  • 24 Dandil E. A Computer-Aided Pipeline for Automatic Lung Cancer Classification on Computed Tomography Scans. J Healthc Eng 2018 Nov 1;2018:9409267.
    PubMed
  • 25 Liu X, Guo S, Yang B, Ma S, Zhang H, Li J, et al. Automatic Organ Segmentation for CT Scans Based on Super-Pixel and Convolutional Neural Networks. J Digit Imaging 2018 Oct;31(5):748-60.
    PubMed
  • 26 Mekali V, Girijamma HA. An Fully Automated CAD System for Juxta-Vacular Nodules Segmentation in CT Scan Images. In: 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC). IEEE; 2019. p. 917-24.
    PubMed
  • 27 Pang T, Guo S, Zhang X, Zhao L. Automatic Lung Segmentation Based on Texture and Deep Features of HRCT Images with Interstitial Lung Disease. Biomed Res Int 2019 Nov 29;2019:2045432.
    PubMed
  • 28 Liu C, Pang M, Zhao R. Novel superpixel-based algorithm for segmenting lung images via convolutional neural network and random forest. IET Image Process 2020;14(16):4340-8.
    PubMed
  • 29 Peng T, Xu TC, Wang Y, Zhou H, Candemir S, Zaki WMDW, et al. Hybrid automatic lung segmentation on chest ct scans. IEEE Access 2020;8:73293-306.
    PubMed
  • 30 Liu C, Zhao R, Pang M. A fully automatic segmentation algorithm for CT lung images based on random forest. Med Phys 2020 Feb;47(2):518-29.
    PubMed
  • 31 Fu Y, Ippolito JE, Ludwig DR, Nizamuddin R, Li HH, Yang D. Technical Note: Automatic segmentation of CT images for ventral body composition analysis. Med Phys 2020 Nov;47(11):5723-30.
    PubMed
  • 32 Pu J, Leader JK, Bandos A, Ke S, Wang J, Shi J, et al. Automated quantification of COVID-19 severity and progression using chest CT images. Eur Radiol 2021 Jan;31(1):436-46.
    PubMed
  • 33 Pawar SP, Talbar SN. LungSeg-Net: Lung field segmentation using generative adversarial network. Biomed Signal Process Control 2021;64:102296.
    PubMed
  • 34 Ronneberger O, Fischer P, BroxT. U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention 2015 – MICCAI. Cham: Springer; 2015. p. 234-41.
    PubMed
  • 35 Mekali V, Girijamma HA. Fully Automatic Detection and Segmentation Approach for Juxta-Pleural Nodules From CT Images. Int J Healthc Inf Syst Inform 2021;16(2):87-104.
    PubMed
  • 36 Ren YH, Sun XW, Nie SD. A 3D segmentation method of lung parenchyma based on CT image sequences. In: 2010 International Conference on Information, Networking and Automation, Vol 2. IEEE; 2010. p. 332-6.
    PubMed
  • 37 Kumar SP, Latte MV. Fully automated segmentation of lung parenchyma using break and repair strategy. Journal of Intelligent Systems 2019;28(2):275-89.
    PubMed
  • 38 Pham DL, Xu C, Prince JL. Current methods in medical image segmentation. Annu Rev Biomed Eng 2000;2:315-37.
    PubMed
  • 39 van Rikxoort EM, de Hoop B, van de Vorst S, Prokop M, van Ginneken B. Automatic segmentation of pulmonary segments from volumetric chest CT scans. IEEE Trans Med Imaging 2009 Apr;28(4):621-30.
    PubMed
  • 40 Zhao JJ, Ji GH, Xia Y, Zhang XL. Cavitary nodule segmentation in computed tomography images based on self–generating neural networks and particle swarm optimisation. International Journal of Bio-Inspired Computation 2015:7(1):62-7.
    PubMed
  • 41 Oliveira AC, Domingues I, Duarte H, Santos J, Abreu PH. Going Back to Basics on Volumetric Segmentation of the Lungs in CT: A Fully Image Processing Based Technique. In: Iberian Conference on Pattern Recognition and Image Analysis 2019. Cham: Springer; 2019. p. 322-34.
    PubMed
  • 42 Bi M, Summers RM, Yao J. Three-dimensional automatic computer-aided evaluation of pleural effusions on chest CT images. In: Medical Imaging 2011: Biomedical Applications in Molecular, Structural, and Functional Imaging, Vol. 7965. SPIE; 2011. p.499-505.
    PubMed
  • 43 Ukil S, Sonka M, Reinhardt JM. Automatic segmentation of pulmonary fissures in X-ray CT images using anatomic guidance. In: Medical Imaging 2006: Image Processing Vol. 6144. SPIE; 2006. p. 213-23.
    PubMed
  • 44 Lassen B, Kuhnigk JM, Friman O, Krass S, Peitgen HO. Automatic segmentation of lung lobes in CT images based on fissures, vessels, and bronchi. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE; 2010. p. 560-3.
    PubMed
  • 45 Devaki K, Bhaskaran V. A novel approach to detect fissures in lung CT images using marker-based watershed transformation, J Comput Sci 2014;10:896–905.
    PubMed
  • 46 Wei Q, Hu Y, MacGregor J, Gelfand G. Segmentation of lung lobes in clinical CT images. Int J Comput Assist Radiol Surg 2008;3(1):151–63.
    PubMed
  • 47 Yoshino Y, Miyajima T, Lu H, Tan J, Kim H, Murakami S, et al. Automatic classification of lung nodules on MDCT images with the temporal subtraction technique. Int J Comput Assist Radiol Surg 2017 Oct;12(10):89-1798.
    PubMed
  • 48 Sundar A, Anitha JA. 3D Lung Segmentation on CT Images Using Region-Based Method. International Journal of Advanced Trends in Computer Science and Engineering 2019;1156–61.
    PubMed
  • 49 Kakar M, Olsen DR. Automatic segmentation and recognition of lungs and lesion from CT scans of thorax. Comput Med Imaging Graph 2009 Jan;33(1):72-82.
    PubMed
  • 50 Abbas Q. Segmentation of differential structures on computed tomography images for diagnosis lung-related diseases. Biomed Signal Process Control 2017;33:325–34.
    PubMed
  • 51 Mao Q, Zhao S, Gong T, Qianqian Z. An Effective Hybrid Windowed Fourier Filtering and Fuzzy C-Mean for Pulmonary Nodule Segmentation. J Med ImagingHealth Inform 2018;8(1):72–7.
    PubMed
  • 52 Khan ZF. Automated Segmentation of Lung Parenchyma Using Colour Based Fuzzy C-Means Clustering. Journal of Electrical Engineering & Technology 2019;14(5):2163–9.
    PubMed
  • 53 Khan ZF. Fully Automated Coronal and Sagittal Chest Segmentation using Colour Features and Fuzzy C-Means Clustering in CT Images. Biomedical and Pharmacology Journal 2019;12(1):259–66.
    PubMed
  • 54 Sahu SP, Agrawal P, Londhe ND, Verma S. A New Hybrid Approach Using Fuzzy Clustering and Morphological Operations for Lung Segmentation in Thoracic CT Images. Biomedical and Pharmacology Journal 2017;10(14):1949–61.
    PubMed
  • 55 Sahu SP, Agrawal P, Londhe ND, Verma S. Lung Segmentation of CT Images Using Fuzzy C-Means for the Detection of Cancer in Early Stages. Advances in Data and Information Sciences 2019:167–76.
    PubMed
  • 56 Sun X, Zhang H, Duan H. 3D computerized segmentation of lung volume with computed tomography. Acad Radiol 2006 Jun;13(6):670-7.
    PubMed
  • 57 Korfiatis P, Skiadopoulos S, Sakellaropoulos P, Kalogeropoulou C, Costaridou L. Combining 2D wavelet edge highlighting and 3D thresholding for lung segmentation in thin-slice CT. Br J Radiol 2007 Dec;80(960):996-1004.
    PubMed
  • 58 Kumar SN, Kavitha V. Automatic segmentation of lung lobes and fissures for surgical planning. In: 2011 International Conference on Emerging Trends in Electrical and Computer Technology. IEEE; 2011. p. 546-50.
    PubMed
  • 59 Wei Q, Hu Y, MacGregor JH, Gelfand G. Segmentation of lung lobes in volumetric CT images for surgical planning of treating lung cancer. In: 2006 International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE 2006. p. 4869-72.
    PubMed
  • 60 Wang J, Betke M, Ko JP. Pulmonary fissure segmentation on CT. Med Image Anal 2006 Aug;10(4):530-47.
    PubMed
  • 61 Xiao R, Zhou J. Pulmonary Fissure Detection in 3D CT Images Using a Multiple Section Model. Algorithms 2019;12(4):75.
    PubMed
  • 62 Liu C, Pang M. Automatic lung segmentation based on image decomposition and wavelet transform. Biomed Signal Process Control 2020;61:102032.
    PubMed
  • 63 Shi Z, Ma J, Zhao M, Liu Y, Feng Y, Zhang M, et al. Many Is Better Than One: An Integration of Multiple Simple Strategies for Accurate Lung Segmentation in CT Images. Biomed Res Int 2016;2016:1480423.
    PubMed
  • 64 Ming JTC, Noor NM, Rijal OM, Kassim RM, Yunus A. Enhanced automatic lung segmentation using graph cut for interstitial lung disease. In: 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES). IEEE; 2014. p. 17-21.
    PubMed
  • 65 Noor NM, Than JC, Rijal OM, Kassim RM, Yunus A, Zeki AA, et al. Automatic lung segmentation using control feedback system: morphology and texture paradigm. J Med Syst 2015 Mar;39(3):22.
    PubMed
  • 66 Ng CR, Than JCM, Noor NM, Rijal OM, Kassim RM, Yunus A. Preliminary 3D performance evaluation on automatic lung segmentation for interstitial lung disease using high resolution. In: TENCON 2017-2017 IEEE Region 10 Conference. IEEE; 2017. p. 187-91.
    PubMed
  • 67 Khan ZF, Kannan A. Intelligent segmentation of medical images using fuzzy bitplane thresholding. Measurement Science Review 2014;14(2):94.
    PubMed
  • 68 Dong J, Lu K, Dai S, Xue J, Zhai R. Auto-segmentation of pathological lung parenchyma based on region growing method. In: International Conference on Internet Multimedia Computing and Service 2017. Singapore: Springer; 2017. p. 241-51.
    PubMed
  • 69 Chunran Y, Yuanvuan W, Yi G. Automatic detection and segmentation of lung nodule on ct images. In: 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). Springer; 2018. p. 1-6.
    PubMed
  • 70 Sun L, Peng Z, Wang Z, Pu H, Guo L, Yuan G, et al. Automatic lung segmentation in chest CT image using morphology. In: 2019 9th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronic Materials and Devices for Sensing and Imaging. SPIE; 2019 Vol. 10843. p. 328-35.
    PubMed
  • 71 Sun Y, Wang J. Automatic method for lung segmentation with juxta-pleural nodules from thoracic CT based on border separation and correction. In: 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE; 2016. p. 330-5.
    PubMed
  • 72 Shakibapour E, Cunha A, Aresta G, Mendonça AM, Campilho A. An unsupervised metaheuristic search approach for segmentation and volume measurement of pulmonary nodules in lung CT scans. Expert Syst Appl 2019;119:415-28.
    PubMed
  • 73 Tavakoli MB, Orooji M, Teimouri M, Shahabifar R. Segmentation of the pulmonary nodule and the attached vessels in the ct scan of the chest using morphological features and topological skeleton of the nodule. IET Image Process 2020;14(8):1520-8.
    PubMed
  • 74 Kumar SP, Latte MV. Lung parenchyma segmentation: fully automated and accurate approach for thoracic CT scan images. IETE J Res 2020;66(3):370-83.
    PubMed
  • 75 Halder A, Chatterjee S, Dey D, Kole S, Munshi S. An adaptive morphology based segmentation technique for lung nodule detection in thoracic CT image. Comput Methods Programs Biomed 2020 Dec;197:105720.
    PubMed
  • 76 Vivanti R, Joskowicz L, Karaaslan OA, Sosna J. Automatic lung tumor segmentation with leaks removal in follow-up CT studies. Int J Comput Assist Radiol Surg 2015 Sep;10(9):1505-14.
    PubMed
  • 77 Devi KY, Sasikala M. Labeling and clustering-based level set method for automated segmentation of lung tumor stages in CT images. J Ambient Intell Humaniz Comput 2021;12(2):2299-309.
    PubMed
  • 78 Song J, Yang C, Fan L, Wang K, Yang F, Liu S, et al. Lung Lesion Extraction Using a Toboggan Based Growing Automatic Segmentation Approach. IEEE Trans Med Imaging 2016 Jan;35(1):337-53.
    PubMed
  • 79 Yim Y, Hong H. A method for smoothing segmented lung boundary in chest CT images. In: Medical Imaging 2007: Image Processing 6512. SPIE; 2007. p. 1172-8.
    PubMed
  • 80 Pu J, Roos J, Yi CA, Napel S, Rubin GD, Paik DS. Adaptive border marching algorithm: automatic lung segmentation on chest CT images. Comput Med Imaging Graph 2008 Sep;32(6):452-62.
    PubMed
  • 81 Pulagam AR, Kande GB, Ede VK, Inampudi RB. Automated Lung Segmentation from HRCT Scans with Diffuse Parenchymal Lung Diseases. J Digit Imaging 2016 Aug;29(4):507-19.
    PubMed
  • 82 Dash JK, Madhavi V, Mukhopadhyay S, Khandelwal N, Kumar P. Segmentation of interstitial lung disease patterns in HRCT images. In: Medical Imaging 2015: Computer-Aided Diagnosis 9414. SPIE; 2015. p. 687-92.
    PubMed
  • 83 Wang J, Guo H. Automatic Approach for Lung Segmentation with Juxta-Pleural Nodules from Thoracic CT Based on Contour Tracing and Correction. Comput Math Methods Med 2016;2016:2962047.
    PubMed
  • 84 Sousa AM, Martins SB, Falcão AX, Reis F, Bagatin E, Irion K. ALTIS: A fast and automatic lung and trachea CT-image segmentation method. Med Phys 2019 Nov;46(11):4970-82.
    PubMed
  • 85 Hua P, Song Q, Sonka M, Hoffman EA, Reinhardt JM. Segmentation of pathological and diseased lung tissue in CT images using a graph-search algorithm. In: 2011 IEEE International Symposium on biomedical imaging: from nano to macro. IEEE; 2011. p. 2072-5.
    PubMed
  • 86 Nimura Y, Hayashi Y, Kitasaka T, Mori K. Automated torso organ segmentation from 3D CT images using structured perceptron and dual decomposition. In: Medical Imaging 2015: Computer-Aided Diagnosis 9414. SPIE; 2015. p. 878-83.
    PubMed
  • 87 Oluyide OM, Tapamo JR, Viriri S. Automatic lung segmentation based on Graph Cut using a distance -constrained energy. IET Computer Vision 2018;12(5):609-15.
    PubMed
  • 88 Cui H, Wang X, Zhou J, Gong G, Eberl S, Yin Y, et al. A topo-graph model for indistinct target boundary definition from anatomical images. Comput Methods Programs Biomed 2018 Jun;159:211-22.
    PubMed
  • 89 Chama CK, Mukhopadhyay S, Biswas PK, Dhara AK, Madaiah MK, Khandelwal N. Automated lung field segmentation in CT images using mean shift clustering and geometrical features. In: Medical Imaging 2013: Computer-Aided Diagnosis 8670. SPIE; 2013. p. 790-9. SPIE.
    PubMed
  • 90 Liao X, Zhao J, Jiao C, Lei L, Qiang Y, Cui Q. A Segmentation Method for Lung Parenchyma Image Sequences Based on Superpixels and a Self-Generating Neural Forest. PLoS One 2016 Aug 17;11(8):e0160556.
    PubMed
  • 91 Chen C, Xiao R, Zhang T, Lu Y, Guo X, Wang J, et al. Pathological lung segmentation in chest CT images based on improved random walker. Comput Methods Programs Biomed 2021 Mar;200:105864.
    PubMed
  • 92 Dey N, Rajinikanth V, Fong SJ, Kaiser MS, Mahmud M. Social Group Optimization-Assisted Kapur’s Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images. Cognit Comput 2020;12(5):1011-23.
    PubMed
  • 93 Oulefki A, Agaian S, Trongtirakul T, Kassah Laouar A. Automatic COVID-19 lung infected region segmentation and measurement using CT-scans images. Pattern Recognit 2021 Jun;114:107747.
    PubMed
  • 94 Cui H, Wang X, Zhou J, Gong G, Eberl S, Yin Y, et al. A topo-graph model for indistinct target boundary definition from anatomical images. Comput Methods Programs Biomed 2018 Jun;159:211-22.
    PubMed
  • 95 Gu S, Wilson D, Wang Z, Bigbee WL, Siegfried J, Gur D, et al. Identification of pulmonary fissures using a piecewise plane fitting algorithm. Comput Med Imaging Graph 2012 Oct;36(7):560-71.
    PubMed
  • 96 Xiao C, Stoel BC, Bakker ME, Peng Y, Stolk J, Staring M. Pulmonary Fissure Detection in CT Images Using a Derivative of Stick Filter. IEEE Trans Med Imaging 2016 Jun;35(6):1488-500.
    PubMed
  • 97 Blaffert T, Barschdorf H, von Berg J, Dries S, Franz A, Klinder T, et al. Lung lobe modeling and segmentation with individualized surface meshes. In: Medical Imaging 2008: Image Processing 6914. SPIE; 2008. p. 493-502.
    PubMed
  • 98 Alilou M, Beig N, Orooji M, Rajiah P, Velcheti V, Rakshit S, et al. An integrated segmentation and shape-based classification scheme for distinguishing adenocarcinomas from granulomas on lung CT. Med Phys 2017 Jul;44(7):3556-69.
    PubMed
  • 99 Farag AA, El-Baz A, Gimel’farb G, Falk R, El-Ghar MA, Eldiasty T, et al. Appearance models for robust segmentation of pulmonary nodules in 3D LDCT chest images. Med Image Comput Comput Assist Interv 2006;9(Pt 1):662-70.
    PubMed
  • 100 El-Baz A, Farag A, Gimel’farb G, Falk R, El-Ghar MA, Eldiasty T. A framework for automatic segmentation of lung nodules from low dose chest CT scans. In: 18th International Conference on Pattern Recognition 2006 (ICPR’06) Vol. 3. IEEE; 2006. p. 611-4.
    PubMed
  • 101 Gill G, Bauer C, Beichel RR. A method for avoiding overlap of left and right lungs in shape model guided segmentation of lungs in CT volumes. Med Phys 2014 Oct;41(10):101908.
    PubMed
  • 102 Pu J, Paik DS, Meng X, Roos JE, Rubin GD. Shape “break-and-repair” strategy and its application to automated medical image segmentation. IEEE Trans Vis Comput Graph 2011 Jan;17(1):115-24.
    PubMed
  • 103 Nimura Y, Hayashi Y, Kitasaka T, Misawa K, Mori K. Automated torso organ segmentation from 3D CT images using conditional random field. In: Medical Imaging 2016: Computer-Aided Diagnosis, Vol. 9785. SPIE; 2016. p. 931-6.
    PubMed
  • 104 Zhang X, Wang J, Yang Y, Wang B, Gu L. Spline curve deformation model with prior shapes for identifying adhesion boundaries between large lung tumors and tissues around lungs in CT images. Med Phys 2020 Mar;47(3):1011-20.
    PubMed
  • 105 Chung H, Ko H, Jeon SJ, Yoon KH, Lee J. Automatic Lung Segmentation With Juxta-Pleural Nodule Identification Using Active Contour Model and Bayesian Approach. IEEE J Transl Eng Health Med 2018 May 18;6:1800513.
    PubMed
  • 106 El-Ba A, Gimel’farb G, Falk R, Holland T, Shaffer T. A new stochastic framework for accurate lung segmentation. Med Image Comput Comput Assist Interv 2008;11(Pt 1):322-30.
    PubMed
  • 107 Zhu Y, Tan Y, Hua Y, Zhang G, Zhang J. Automatic segmentation of ground-glass opacities in lung CT images by using Markov random field-based algorithms. J Digit Imaging 2012 Jun;25(3):409-22.
    PubMed
  • 108 Soliman A, Khalifa F, Elnakib A, Abou El-Ghar M, Dunlap N, Wang B, et al. Accurate Lungs Segmentation on CT Chest Images by Adaptive Appearance-Guided Shape Modeling. IEEE Trans Med Imaging 2017 Jan;36(1):263-76.
    PubMed
  • 109 Yao J, Han W, Summers RM. Computer Aided Evaluation of Pleural Effusion Using Chest CT Images. Proc IEEE Int Symp Biomed Imaging 2009;2009:241-4.
    PubMed
  • 110 Sun S, Bauer C, Beichel R. Automated 3-D segmentation of lungs with lung cancer in CT data using a novel robust active shape model approach. IEEE Trans Med Imaging 2012 Feb;31(2):449-60.
    PubMed
  • 111 He N, Zhang X, Zhao J, Zhao H, Qiang Y. Pulmonary parenchyma segmentation in thin CT image sequences with spectral clustering and geodesic active contour model based on similarity. In: Ninth International Conference on Digital Image Processing 2017 (ICDIP 2017), Vol. 10420. SPIE; 2017. p. 515-23.
    PubMed
  • 112 Zhang W, Wang X, Zhang P, Chen J. Global optimal hybrid geometric active contour for automated lung segmentation on CT images. Comput Biol Med 2017 Dec 1;91:168-80.
    PubMed
  • 113 Zhang S, Chen X, Zhu Z, Feng B, Chen Y, Long W. Segmentation of small ground glass opacity pulmonary nodules based on Markov random field energy and Bayesian probability difference. Biomed Eng Online 2020 Jun 17;19(1):51.
    PubMed
  • 114 Zhou J, Yan Z, Lasio G, Huang J, Zhang B, Sharma N, et al. Automated compromised right lung segmentation method using a robust atlas-based active volume model with sparse shape composition prior in CT. Comput Med Imaging Graph 2015 Dec;46 Pt 1:47-55.
    PubMed
  • 115 Ciardo D, Gerardi MA, Vigorito S, Morra A, Dell’acqua V, Diaz FJ, et al. Atlas-based segmentation in breast cancer radiotherapy: Evaluation of specific and generic-purpose atlases. Breast 2017 Apr;32:44-52.
    PubMed
  • 116 Oliveira B, Queirós S, Morais P, Torres HR, Gomes-Fonseca J, Fonseca JC, et al. A novel multi-atlas strategy with dense deformation field reconstruction for abdominal and thoracic multi-organ segmentation from computed tomography. Med Image Anal 2018 Apr;45:108-20.
    PubMed
  • 117 Chen S, Endres J, Dorn S, Maier J, Lell M, Kachelrieß M, et al. A feasibility study of automatic multi-organ segmentation using probabilistic atlas. In: Bildverarbeitung für die Medizin. Berlin, Heidelberg: Springer Verlag; 2017. p. 218-23.
    PubMed
  • 118 Agarwala S, Nandi D, Kumar A, Dhara AK, Sadhu SBTA, Bhadra AK. Automated segmentation of lung field in HRCT images using active shape model. In: TENCON 2017-2017 IEEE Region 10 Conference. IEEE; 2017. p. 2516-20.
    PubMed
  • 119 Shi C, Cheng Y, Wang J, Wang Y, Mori K, Tamura S. Low-rank and sparse decomposition based shape model and probabilistic atlas for automatic pathological organ segmentation. Med Image Anal 2017 May;38:30-49.
    PubMed
  • 120 Ryan SM, Vestal B, Maier LA, Carlson NE, Muschelli J. Template Creation for High-Resolution Computed Tomography Scans of the Lung in R Software. Acad Radiol 2020 Aug;27(8):e204-e215.
    PubMed
  • 121 Dhalia Sweetlin J, Khanna Nehemiah H, Kannan A. Patient-Specific Model Based Segmentation of Lung Computed Tomographic Images. Journal of Information Science & Engineering 2016;32(5):1373–94.
    PubMed
  • 122 Wittenstein O, Hiepe P, Sowa LH, Karsten E, Fandrich I, Dunst J. Automatic image segmentation based on synthetic tissue model for delineating organs at risk in spinal metastasis treatment planning. Strahlenther Onkol 2019 Dec;195(12):1094-103.
    PubMed
  • 123 Guo Y, Zhou C, Chan HP, Chughtai A, Wei J, Hadjiiski LM, et al. Automated iterative neutrosophic lung segmentation for image analysis in thoracic computed tomography. Med Phys 2013 Aug;40(8):081912.
    PubMed
  • 124 Mansoor A, Bagci U, Xu Z, Foster B, Olivier KN, Elinoff JM, et al. A generic approach to pathological lung segmentation. IEEE Trans Med Imaging 2014 Dec;33(12):2293-310.
    PubMed
  • 125 özsavas EE, Telatar Z, Dirican B, Sager ö, Beyzadeoglu M. Automatic segmentation of anatomical structures from CT scans of thorax for RTP. Comput Math Methods Med 2014;2014:472890.
    PubMed
  • 126 Ukil S, Hoffman EA, Reinhardt JM. Automatic lung lobe segmentation in X-ray CT images by 3D watershed transform using anatomic information from the segmented airway tree. In: Medical Imaging 2005: Image Processing 5747. SPIE; 2005. p. 556-67.
    PubMed
  • 127 Qi S, van Triest HJ, Yue Y, Xu M, Kang Y. Automatic pulmonary fissure detection and lobe segmentation in CT chest images. Biomed Eng Online 2014 May 7;13:59.
    PubMed
  • 128 Zhou X, Hayashi T, Hara T, Fujita H, Yokoyama R, Kiryu T, et al. Automatic recognition of lung lobes and fissures from multislice CT images. In: Medical Imaging 2004: Image Processing 5370. SPIE; 2004. p. 1629-33.
    PubMed
  • 129 Saad M, Lee IH, Choi TS. Automated delineation of non -small cell lung cancer: A step toward quantitative reasoning in medical decision science. Int J Imaging Syst Technol 2019;29(4):561-76.
    PubMed
  • 130 Cai W, Lee EY, Vij A, Mahmood SA, Yoshida H. MDCT for computerized volumetry of pneumothoraces in pediatric patients. Acad Radiol 2011 Mar;18(3):315-23.
    PubMed
  • 131 Gordaliza PM, Muñoz-Barrutia A, Abella M, Desco M, Sharpe S, Vaquero JJ. Unsupervised CT Lung Image Segmentation of a Mycobacterium Tuberculosis Infection Model. Sci Rep 2018 Jun 28;8(1):9802.
    PubMed
  • 132 Spanier AB, Joskowicz L. Rule-based ventral cavity multi-organ automatic segmentation in CT scans. In: International MICCAI workshop on medical computer vision 2014. Cham: Springer; 2014. p. 163-70.
    PubMed
  • 133 Konietzke P, Weinheimer O, Wielpütz MO, Savage D, Ziyeh T, Tu C, et al. Validation of automated lobe segmentation on paired inspiratory-expiratory chest CT in 8-14 year-old children with cystic fibrosis. PLoS One 2018 Apr 9;13(4):e0194557.
    PubMed
  • 134 Ukil S, Reinhardt JM. Smoothing lung segmentation surfaces in 3D X-ray CT images using anatomic guidance. In: Medical Imaging 2004: Image Processing 5370. SPIE; 2004. p. 1066-75.
    PubMed
  • 135 Massoptier L, Misra A, Sowmya A, Casciaro S. Combining Graph-Cut Technique and Anatomical Knowledge for Automatic Segmentation of Lungs Affected By Diffuse Parenchymal Disease in HRCT Images. Int J Image Graph 2011;11(04):509-29.
    PubMed
  • 136 Zheng T, Oda M, Wang C, Moriya T, Hayashi Y, Otake Y, et al. Unsupervised segmentation of COVID-19 infected lung clinical CT volumes using image inpainting and representation learning. In: Medical Imaging 2021: Image Processing 11596. SPIE; 2021. p. 931-6. SPIE.
    PubMed
  • 137 Mansoor A, Bagci U, Xu Z, Foster B, Olivier KN, Elinoff JM, et al. Correction to “a generic approach to pathological lung segmentation”. IEEE Trans Med Imaging 2015 Jan;34(1):354.
    PubMed
  • 138 Korfiatis P, Kazantzi A, Kalogeropoulou C, Petsas T, Costaridou L. (2010, November). Optimizing lung volume segmentation by texture classification. ITAB Corfu Greece 2010:1-4.
    PubMed
  • 139 Korfiatis, P Kalogeropoulou C, Karahaliou A, Kazantzi A, Skiadopoulos S, Costaridou L. Texture classification-based segmentation of lung affected by interstitial pneumonia in high-resolution CT. Med Phys 2008 Dec;35(12):5290-302.
    PubMed
  • 140 Tao Y, Lu L, Dewan M, Chen AY, Corso J, Xuan J, et al. Multi-level ground glass nodule detection and segmentation in CT lung images. Med Image Comput Comput Assist Interv 2009;12(Pt 2):715-23.
    PubMed
  • 141 Wang J, Li F, Li Q. Usefulness of texture features for segmentation of lungs with severe diffuse interstitial lung disease. In: Medical Imaging 2010: Computer-Aided Diagnosis 7624. SPIE; 2020. p. 824-31.
    PubMed
  • 142 Polan DF, Brady SL, Kaufman RA. Tissue segmentation of computed tomography images using a Random Forest algorithm: a feasibility study. Phys Med Biol 2016;61(17):6553.
    PubMed
  • 143 Somasundaram E, Deaton J, Kaufman R, Brady S. Fully automated tissue classifier for contrast-enhanced CT scans of adult and pediatric patients. Phys Med Biol 2018;63(13):135009.
    PubMed
  • 144 Zhou Y, Xie L, Shen W, Wang Y, Fishman EK, Yuille AL. A fixed-point model for pancreas segmentation in abdominal CT scans. In International conference on medical image computing and computer-assisted intervention – MICCAI – 2017;10433. Cham: Springer; 2017. p. 693-701.
    PubMed
  • 145 Goodfellow I, Bengio Y, Courville A. Deep Learning. MIT Press; 2016.
    PubMed
  • 146 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 Feb;58(2):257-64.
    PubMed
  • 147 Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention – MICCAI – 2015. Cham: Springer; 2015. p. 234-41.
    PubMed
  • 148 Sudre CH, Li W, Vercauteren T, Ourselin S, Jorge Cardoso M. Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. Deep Learn Med Image Anal Multimodal Learn Clin Decis Support 2017;2017:240-8.
    PubMed
  • 149 Carvalho JB, Moreira JM, Figueiredo MA, Papanikolaou N. Automatic detection and segmentation of lung lesions using deep residual CNNs. In: 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE). IEEE; 2019. p. 977-83.
    PubMed
  • 150 Shaziya H, Shyamala K, Zaheer R. Automatic lung segmentation on thoracic CT scans using U-net convolutional network. In: 2018 International conference on communication and signal processing (ICCSP). IEEE; 2018. p. 643-7.
    PubMed
  • 151 Zhu J, Liu Y, Zhang J, Wang Y, Chen L. Preliminary Clinical Study of the Differences Between Interobserver Evaluation and Deep Convolutional Neural Network-Based Segmentation of Multiple Organs at Risk in CT Images of Lung Cancer. Front Oncol 2019 Jul 5;9:627.
    PubMed
  • 152 Kumar A, Agarwala S, Dhara AK, Nandi D, Thakur SB, Bhadra AK, et al. Segmentation of lung field in HRCT images using U-net based fully convolutional networks. In: Annual Conference on Medical Image Understanding and Analysis 2018;894. Cham: Springer: 2018. p. 84-93.
    PubMed
  • 153 Kunpeng Z, Xin S. Automatic lung field segmentation based on the U-net deep neural network. In: 2019 14th IEEE International Conference on Electronic Measurement & Instruments (ICEMI). IEEE; 2019. p. 1670-6.
    PubMed
  • 154 Vinushree S, Gowda RM. Segmentation of Lung Cancer Using Deep Learning. International Journal of Recent Technology and Engineering (IJRTE) 2019;08:1188–92.
    PubMed
  • 155 Chen X, Zhao H, Zhou P. Lung Lobe Segmentation Based on Lung Fissure Surface Classification Using a Point Cloud Region Growing Approach. Algorithms 2020;13(10):263.
    PubMed
  • 156 Comelli A, Coronnello C, Dahiya N, Benfante V, Palmucci S, Basile A, et al. (2020). Lung segmentation on high-resolution computerized tomography images using deep learning: a preliminary step for radiomics studies. J Imaging 2020;6(11):125.
    PubMed
  • 157 Ghosh S, Sil S, Gomes RM, Dey M. Using convolutions and image processing techniques to segment lungs from ct data. In: Emerging Technology in Modelling and Graphics. Singapore: Springer; 2020. p. 129-36.
    PubMed
  • 158 Lashchenova D, Gromov A, Konushin A, Mesheryakova A. Segmentation of lungs, lesions, and lesion types on chest CT scans of patients with COVID-19. In: 2020 CEUR Workshop Proc. Graphicon-Conference on Computer Graphics and Vision; 2020. p. 20-1.
    PubMed
  • 159 Peng Z, Fang X, Yan P, Shan H, Liu T, Pei X, et al. A method of rapid quantification of patient-specific organ doses for CT using deep-learning-based multi-organ segmentation and GPU-accelerated Monte Carlo dose computing. Med Phys 2020 Jun;47(6):2526-36.
    PubMed
  • 160 Shaziya H, Shyamala K. Pulmonary CT Images Segmentation using CNN and UNet Models of Deep Learning. In: 2020 IEEE Pune Section International Conference (PuneCon) 2020. p. 195–201.
    PubMed
  • 161 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 Computing and Applications 2020;32:15845–55.
    PubMed
  • 162 Vu CC, Siddiqui ZA, Zamdborg L, Thompson AB, Quinn TJ, Castillo E, et al. Deep convolutional neural networks for automatic segmentation of thoracic organs-at-risk in radiation oncology - use of non-domain transfer learning. J Appl Clin Med Phys 2020 Jun;21(6):108-13.
    PubMed
  • 163 Iyer TJ, Raj ANJ, Ghildiyal S, Nersisson R. Performance analysis of lightweight CNN models to segment infectious lung tissues of COVID-19 cases from tomographic images. PeerJ Computer Science 2021;7:e368.
    PubMed
  • 164 Jalali Y, Fateh M, Rezvani M, Abolghasemi V, Anisi MH. ResBCDU-Net: A Deep Learning Framework for Lung CT Image Segmentation. Sensors (Basel) 2021 Jan 3;21(1):268.
    PubMed
  • 165 Saood A, Hatem I. COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet. BMC Med Imaging 2021 Feb 9;21(1):19.
    PubMed
  • 166 Imran AAZ, Hatamizadeh A, Ananth SP, Ding X, Terzopoulos D, Tajbakhsh N. Automatic Segmentation of Pulmonary Lobes Using a Progressive Dense V-Network. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support 2018;: 282–90.
    PubMed
  • 167 Ferreira FT, Sousa P, Galdran A, Sousa MR, Campilho A. End-to-End Supervised Lung Lobe Segmentation. In: 2018 International Joint Conference on Neural Networks (IJCNN); 2018. p. 1–8.
    PubMed
  • 168 Negahdar M, Beymer D, Syeda-Mahmood T. Automated volumetric lung segmentation of thoracic CT images using fully convolutional neural network. In: Medical Imaging 2018: Computer-Aided Diagnosis; 2018, 10575. p. 356–61.
    PubMed
  • 169 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 May;46(5):2169-80.
    PubMed
  • 170 Fu W, Sharma S, Smith T, Hou R, Abadi E, Selvakumaran V, et al. (2019, March). Multi-organ segmentation in clinical-computed tomography for patient-specific image quality and dose metrology. In: Medical Imaging 2019: Physics of Medical Imaging. SPIE 2019. p. 568-73.
    PubMed
  • 171 Nakano R, Arimura H, Haekal M, Ohga S. Automated segmentation framework of lung gross tumor volumes on 3D planning CT images using dense V-Net deep learning. In: International Forum on Medical Imaging in Asia 2019; 2019, 11050. p. 157–60.
    PubMed
  • 172 Sousa P, Galdran A, Costa P, Campilho A. Learning to Segment the Lung Volume from CT Scans Based on Semi-Automatic Ground-Truth. In: 2019 IEEE 16th International Symposium on Biomedical Imaging. ISBI: 2019. p. 1202–6.
    PubMed
  • 173 Imran AAZ, Hatamizadeh A, Ananth SP, Ding X, Tajbakhsh N, Terzopoulos D. Fast and automatic segmentation of pulmonary lobes from chest CT using a progressive dense V-network. Comput Methods Biomech Biomed Eng Imaging Vis 2020;8(5):509–18.
    PubMed
  • 174 Funke W, Veasey B, Zurada J, Frigui H, Amini A. 3D U-Net for segmentation of pulmonary nodules in volumetric CT scans from multi-annotator truth estimation. In: Medical Imaging 2020: Computer-Aided Diagnosis. SPIE; 2020; 11314:1131429.
    PubMed
  • 175 Park J, Yun J, Kim N, Park B, Cho Y, Park HJ, 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 Feb;33(1):221-30.
    PubMed
  • 176 Gu D, Chen L, Shan F, Xia L, Liu J, Mo Z, et al. Computing infection distributions and longitudinal evolution patterns in lung CT images. BMC Med Imaging 2021 Mar 23;21(1):57.
    PubMed
  • 177 Zhang Z, Ren J, Tao X, Tang W, Zhao S, Zhou L, et al. Automatic segmentation of pulmonary lobes on low-dose computed tomography using deep learning. Ann Transl Med 2021 Feb;9(4):291.
    PubMed
  • 178 Cui Y, Arimura H, Nakano R, Yoshitake T, Shioyama Y, Yabuuchi H. Automated approach for segmenting gross tumor volumes for lung cancer stereotactic body radiation therapy using CT-based dense V-networks. J Radiat Res 2021 Mar 10;62(2):346-55.
    PubMed
  • 179 Shan F, Gao Y, Wang J, Shi W, Shi N, Han M, et al. Abnormal lung quantification in chest CT images of COVID-19 patients with deep learning and its application to severity prediction. Med Phys 2021 Apr;48(4):1633-45.
    PubMed
  • 180 Wang S, Zhou M, Liu Z, Liu Z, Gu D, Zang Y, et al. Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation. Med Image Anal 2017 Aug;40:172-83.
    PubMed
  • 181 Chen W, Wei H, Peng S, Sun J, Qiao X, Liu X. HSN: Hybrid Segmentation Network for Small Cell Lung Cancer Segmentation. IEEE Access 2019;7:75591–603.
    PubMed
  • 182 Tan W, Liu Y, Liu H, Yang J, Yin X, Zhang Y. A Segmentation Method of Lung Parenchyma From Chest CT Images Based on Dual U-Net. In: 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE: 2019. p. 1649–56.
    PubMed
  • 183 Prasad JMN, Krishna MV. Segmentation of Lung Ct Images using Cascaded Fully Convolutional Neural Networks. Recent Technology and Engineering 2019;8(2):1–3.
    PubMed
  • 184 Li Q, Chen L, Li X, Xia S, Kang Y. An improved random forests approach for interactive lobar segmentation on emphysema detection. Granular Computing 2020;5:1–10.
    PubMed
  • 185 Sun Y, Tang J, Lei W, He D. 3D Segmentation of Pulmonary Nodules Based on Multi-View and Semi-Supervised. IEEE Access 2020;8:26457–67.
    PubMed
  • 186 Wu Y, Lin L. Automatic Lung Segmentation in CT Images Using Dilated Convolution Based Weighted Fully Convolutional Network. J Phys Conf Ser 2020;1646:12032.
    PubMed
  • 187 Ghomi Z, Mirshahi R, Khameneh Bagheri A, Fattahpour A, Mohammadiun S, Alavi Gharahbagh A, et al. Segmentation of COVID-19 pneumonia lesions: A deep learning approach. Med J Islam Repub Iran 2020 Dec 22;34:174.
    PubMed
  • 188 Zhou L, Li Z, Zhou J, Li H, Chen Y, Huang Y, et al. A Rapid, Accurate and Machine-Agnostic Segmentation and Quantification Method for CT-Based COVID-19 Diagnosis. IEEE Trans Med Imaging 2020 Aug;39(8):2638-52.
    PubMed
  • 189 Yoo SJ, Yoon SH, Lee JH, Kim KH, Choi HI, Park SJ, et al. Automated Lung Segmentation on Chest Computed Tomography Images with Extensive Lung Parenchymal Abnormalities Using a Deep Neural Network. Korean J Radiol 2021 Mar;22(3):476-88.
    PubMed
  • 190 Wu D, Gong K, Arru CD, Homayounieh F, Bizzo B, Buch V, et al. Severity and Consolidation Quantification of COVID-19 From CT Images Using Deep Learning Based on Hybrid Weak Labels. IEEE J Biomed Health Inform 2020 Dec;24(12):3529-38.
    PubMed
  • 191 Huang CH, Xiao WT, Chang LG, Tsai WT, Liu WM. Automatic tissue segmentation by deep learning: from colorectal polyps in colonoscopy to abdominal organs in CT exam. In: 2018 IEEE Visual Communications and Image Processing (VCIP). IEEE: 2018. p. 1–4.
    PubMed
  • 192 El-Bana S, Al-Kabbany A, Sharkas M. A Two-Stage Framework for Automated Malignant Pulmonary Nodule Detection in CT Scans. Diagnostics (Basel) 2020 Feb 28;10(3):131.
    PubMed
  • 193 de Freitas Souza LF, Silva ICL, Marques AG, Silva FHDS, Nunes VX, et al. Internet of Medical Things: An Effective and Fully Automatic IoT Approach Using Deep Learning and Fine-Tuning to Lung CT Segmentation. Sensors (Basel) 2020 Nov 24;20(23):6711.
    PubMed
  • 194 Huang X, Sun W, Tseng TB, Li C, Qian W. Fast and fully-automated detection and segmentation of pulmonary nodules in thoracic CT scans using deep convolutional neural networks. Comput Med Imaging Graph 2019 Jun;74:25-36.
    PubMed
  • 195 Wang X, Teng P, Lo P, Banola A, Kim G, Abtin F, et al. High throughput lung and lobar segmentation by 2D and 3D CNN on chest CT with diffuse lung disease. Image Anal Mov Organ Breast Thorac Images 2018:202-14.
    PubMed
  • 196 Hu Q, de F Souza LF, Holanda GB, Alves SSA, Dos S Silva FH, Han T, et al. An effective approach for CT lung segmentation using mask region-based convolutional neural networks. Artif Intell Med 2020 Mar;103:101792.
    PubMed
  • 197 Liu C, Pang M. Extracting Lungs from CT Images via Deep Convolutional Neural Network Based Segmentation and Two-Pass Contour Refinement. J Digit Imaging 2020 Dec;33(6):1465-78.
    PubMed
  • 198 Tang H, Zhang C, Xie X. Automatic Pulmonary Lobe Segmentation Using Deep Learning. Proc IEEE Int Symp Biomed Imaging 2019:1225-8.
    PubMed
  • 199 Jiang J, Hu YC, Liu CJ, Halpenny D, Hellmann MD, Deasy JO, et al. Multiple Resolution Residually Connected Feature Streams for Automatic Lung Tumor Segmentation From CT Images. IEEE Trans Med Imaging 2019 Jan;38(1):134-44.
    PubMed
  • 200 Wang W, Chen J, Zhao J, Chi Y, Xie X, Zhang L, et al. Automated segmentation of pulmonary lobes using coordination-guided deep neural networks. Proc IEEE Int Symp Biomed Imaging 2019:1353-7.
    PubMed
  • 201 George K, Harrison A, Jin D, Xu Z, Mollura D. Pathological Pulmonary Lobe Segmentation from CT Images Using Progressive Holistically Nested Neural Networks and Random Walker. Deep Learn Med Image Anal Multimodal Learn Clin Decis Support 2018:195–203.
    PubMed
  • 202 Kumar Singh V, Abdel-Nasser M, Pandey N, Puig D. LungINFseg: Segmenting COVID-19 Infected Regions in Lung CT Images Based on a Receptive-Field-Aware Deep Learning Framework. Diagnostics (Basel) 2021 Jan 22;11(2):158.
    PubMed
  • 203 Aresta G, Jacobs C, Araújo T, Cunha A, Ramos I, van Ginneken B, et al. iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network. Sci Rep 2019 Aug 12;9(1):11591.
    PubMed
  • 204 Alves JH, Neto PMM, Oliveira LF. Extracting Lungs from CT Images Using Fully Convolutional Networks. In 2018 International Joint Conference on Neural Networks (IJCNN). IEEE: 2018. p. 1–8.
    PubMed
  • 205 Hossain S, Najeeb S, Shahriyar A, Abdullah ZR, Ariful Haque M. A Pipeline for Lung Tumor Detection and Segmentation from CT Scans Using Dilated Convolutional Neural Networks. Proc IEEE Int Conf Acoust Speech Signal Process 2019:1348–52.
    PubMed
  • 206 Anderson O, Kidd AC, Goatman KA, Weir AJ, Voisey J, Dilys V, et al. (2020). Fully Automated Volumetric Measurement of Malignant Pleural Mesothelioma from Computed Tomography Images by Deep Learning: Preliminary Results of an Internal Validation. In: 7th International Conference on Bioimaging; 2020. p. 64–73.
    PubMed
  • 207 Song J, Tian Z, Zhang C, Zheng Y, Yu X, Shi Z. Higher accuracy and lower complexity: convolutional neural network for multi-organ segmentation. In: International Symposium on Artificial Intelligence and Robotics 2020, vol 11574. SPIE: 2020. p. 54–59.
    PubMed
  • 208 Javaid U, Dasnoy D, Lee JA. Multi-organ Segmentation of Chest CT Images in Radiation Oncology: Comparison of Standard and Dilated UNet. In: Advanced Concepts for Intelligent Vision Systems; 2018. p. 188–99.
    PubMed
  • 209 Oda M, Hayashi Y, Otake Y, Hashimoto M, Akashi T, Mori K. Lung infection and normal region segmentation from CT volumes of COVID-19 cases. In: Medical Imaging 2021: Computer-Aided Diagnosis 2021, vol. 11597. SPIE: 2021. p. 682–7.
    PubMed
  • 210 Zhou Y, Chen M, Zhang M, Wang T, Yan F, Xie C. Automatic Segmentation of Lung Noudles using improved U-Net NetWork. In: 2020 Chinese Automation Congress (CAC); 2020. p. 1609–13.
    PubMed
  • 211 Liu J, Wang C, Guo J, Shao J, Xu X, Liu X, et al. RPLS-Net: pulmonary lobe segmentation based on 3D fully convolutional networks and multi-task learning. Int J Comput Assist Radiol Surg 2021 Jun;16(6):895-904.
    PubMed
  • 212 Singh J, Tripathy A, Garg P, Kumar A. Lung tuberculosis detection using anti-aliased convolutional networks. Procedia Computer Science 2020;173:281–90.
    PubMed
  • 213 Singadkar G, Mahajan A, Thakur M, Talbar S. Deep Deconvolutional Residual Network Based Automatic Lung Nodule Segmentation. J Digit Imaging 2020 Jun;33(3):678-84.
    PubMed
  • 214 Joseph Raj AN, Zhu H, Khan A, Zhuang Z, Yang Z, Mahesh VGV, et al. ADID-UNET-a segmentation model for COVID-19 infection from lung CT scans. PeerJ Comput Sci 2021 Jan 26;7:e349.
    PubMed
  • 215 Zheng S, Nie W, Pan L, Zheng B, Shen Z, Huang L, et al. A dual -attention V -network for pulmonary lobe segmentation in CT scans. IET Image Processing 2021;15(8):1644-54.
    PubMed
  • 216 Budak ü, Çibuk M, Cömert Z, Sengür A. Efficient COVID-19 Segmentation from CT Slices Exploiting Semantic Segmentation with Integrated Attention Mechanism. J Digit Imaging 2021 Apr;34(2):263-72.
    PubMed
  • 217 Chatzitofis A, Cancian P, Gkitsas V, Carlucci A, Stalidis P, Albanis G, et al. Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment. Int J Environ Res Public Health 2021 Mar 11;18(6):2842.
    PubMed
  • 218 Kamal U, Rafi AM, Hoque R, Wu J, Hasan Md K. Lung Cancer Tumor Region Segmentation Using Recurrent 3D-DenseUNet. Thoracic Image Analysis 2020:36–47.
    PubMed
  • 219 Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 2021 Feb;18(2):203-11.
    PubMed
  • 220 Gerard SE, Herrmann J, Xin Y, Martin KT, Rezoagli E, Ippolito D, et al. CT image segmentation for inflamed and fibrotic lungs using a multi-resolution convolutional neural network. Sci Rep 2021 Jan 14;11(1):1455.
    PubMed
  • 221 Gerard SE, Patton TJ, Christensen GE, Bayouth JE, Reinhardt JM. FissureNet: A Deep Learning Approach For Pulmonary Fissure Detection in CT Images. IEEE Trans Med Imaging 2019 Jan;38(1):156-66.
    PubMed
  • 222 Gerard SE, Reinhardt JM. Pulmonary Lobe Segmentation Using A Sequence of Convolutional Neural Networks For Marginal Learning. Proc IEEE Int Symp Biomed Imaging 2019:1207-11.
    PubMed
  • 223 Amyar A, Modzelewski R, Li H, Ruan S. Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation. Comput Biol Med 2020 Nov;126:104037.
    PubMed
  • 224 Chassagnon G, Vakalopoulou M, Régent A, Zacharaki EI, Aviram G, Martin C, et al. Deep Learning-based Approach for Automated Assessment of Interstitial Lung Disease in Systemic Sclerosis on CT Images. Radiol Artif Intell 2020 Jul 15;2(4):e190006.
    PubMed
  • 225 Paluru N, Dayal A, Jenssen HB, Sakinis T, Cenkeramaddi LR, Prakash J, et al. Anam-Net: Anamorphic Depth Embedding-Based Lightweight CNN for Segmentation of Anomalies in COVID-19 Chest CT Images. IEEE Trans Neural Netw Learn Syst 2021 Mar;32(3):932-46.
    PubMed
  • 226 Yan Q, Wang B, Gong D, Luo C, Zhao W, Shen J, et al. COVID-19 chest CT image segmentation network by multi-scale fusion and enhancement operations. IEEE Trans Big Data 2021;7(1):13-24.
    PubMed
  • 227 Creswell A, White T, Dumoulin V, Arulkumaran K, Sengupta B, Bharath AA. Generative adversarial networks: An overview. IEEE Signal Process Mag 2018;35(1):53–65.
    PubMed
  • 228 Lei Y, Liu Y, Dong X, Tian S, Wang T, Jiang X, et al. Automatic multi-organ segmentation in thorax CT images using U-Net-GAN. In: Proc. Medical Imaging 2019: Computer-Aided Diagnosis. SPIE:2019;1095010.
    PubMed
  • 229 Song J, Huang SC, Kelly B, Liao G, Shi J, Wu N, et al. Automatic Lung Nodule Segmentation and Intra-Nodular Heterogeneity Image Generation. IEEE J Biomed Health Inform 2022 Jun;26(6):2570-81.
    PubMed
  • 230 Zhou B, Crawford R, Dogdas B, Goldmacher G, Chen A. A Progressively-Trained Scale-Invariant and Boundary-Aware Deep Neural Network for the Automatic 3D Segmentation of Lung Lesions. IEEE Winter Conf Appl Comput Vis 2019:1–10.
    PubMed
  • 231 Liu M, Jiang X, Liu Y, Zhao F, Zhou H. A semi-supervised convolutional transfer neural network for 3D pulmonary nodules detection. Neurocomputing 2020;391:199-209.
    PubMed
  • 232 Ram S, Humphries SM, Lynch DA, Galban CG, Hatt CR. Lung Lobe Segmentation With Automated Quality Assurance Using Deep Convolutional Neural Networks. In: 2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops). IEEE: 2020. p. 1–4.
    PubMed
  • 233 Hoebel K, Andrearczyk V, Beers A, Patel J, Chang K, Depeursinge A, et al. An exploration of uncertainty information for segmentation quality assessment. In: Medical Imaging 2020: Image Processing;11313:381-90.
    PubMed
  • 234 Ma J, Nie Z, Wang C, Dong G, Zhu Q, He J, et al. Active contour regularized semi-supervised learning for COVID-19 CT infection segmentation with limited annotations. Phys Med Biol 2020 Dec 18;65(22):225034.
    PubMed
  • 235 Konar D, Panigrahi BK, Bhattacharyya S, Dey N, Jiang R. Auto-Diagnosis of COVID-19 Using Lung CT Images With Semi-Supervised Shallow Learning Network. IEEE Access 2021;9:28716-28.
    PubMed
  • 236 Pezzano G, Ribas Ripoll V, Radeva P. CoLe-CNN: Context-learning convolutional neural network with adaptive loss function for lung nodule segmentation. Comput Methods Programs Biomed 2021 Jan;198:105792.
    PubMed
  • 237 Wang G, Liu X, Li C, Xu Z, Ruan J, Zhu H, et al. A Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions From CT Images. IEEE Trans Med Imaging 2020 Aug;39(8):2653-63.
    PubMed
  • 238 Anastasopoulos C, Weikert T, Yang S, Abdulkadir A, Schmülling L, Bühler C, et al. Development and clinical implementation of tailored image analysis tools for COVID-19 in the midst of the pandemic: The synergetic effect of an open, clinically embedded software development platform and machine learning. Eur J Radiol 2020 Oct;131:109233.
    PubMed
  • 239 Zhang C, Yang G, Cai C, Xu Z, Wu H, Guo Y, et al. Development of a quantitative segmentation model to assess the effect of comorbidity on patients with COVID-19. Eur J Med Res 2020 Oct 12;25(1):49.
    PubMed
  • 240 Lessmann N, Sánchez CI, Beenen L, Boulogne LH, Brink M, Calli E, et al. Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence. Radiology 2021 Jan;298(1):E18-E28.
    PubMed
  • 241 Wang B, Jin S, Yan Q, Xu H, Luo C, Wei L, et al. AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system. Appl Soft Comput 2021 Jan;98:106897.
    PubMed
  • 242 Zhou X, Takayama R, Wang S, Hara T, Fujita H. Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method. Med Phys 2017 Oct;44(10):5221-33.
    PubMed
  • 243 Kumar A, Agarwala S, Dhara AK, Nandi D, Thakur SB, Bhadra AK, et al. (2018, July). Segmentation of lung field in HRCT images using U-net based fully convolutional networks. Med Image Underst Anal 2018;894:84–93.
    PubMed
  • 244 Wang W, Feng R, Chen J, Lu Y, Chen T, Yu H, et al. Nodule-plus R-CNN and deep self-paced active learning for 3D instance segmentation of pulmonary nodules. IEEE Access 2019;7:128796-805.
    PubMed
  • 245 Yu CY, Cheng YC, Kuo C. Early Pulmonary Embolism Detection from Computed Tomography Pulmonary Angiography Using Convolutional Neural Networks. In: 2020 Joint 9th International Conference on Informatics, Electronics Vision (ICIEV) and 2020 4th International Conference on Imaging, Vision Pattern Recognition (icIVPR). IEEE: 2020. p. 1-6.
    PubMed
  • 246 Chen Y, Wang Y, Hu F, Wang D. A Lung Dense Deep Convolution Neural Network for Robust Lung Parenchyma Segmentation. IEEE Access 2020;8:93527-47.
    PubMed
  • 247 Li Z, Zhong Z, Li Y, Zhang T, Gao L, Jin D, et al. From community-acquired pneumonia to COVID-19: a deep learning-based method for quantitative analysis of COVID-19 on thick-section CT scans. Eur Radiol 2020 Dec;30(12):6828-37.
    PubMed
  • 248 Anguita D, Ghelardoni L, Ghio A, Oneto L, Ridella S. The ’K’in K-fold cross validation. In: 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN); 2012. p. 441-6.
    PubMed
  • 249 Karwoski RA, Bartholmai R, Zavaletta VA, Holmes D, Robb RA. Processing of CT images for analysis of diffuse lung disease in the lung tissue research consortium. In: Medical Imaging 2008: Physiology, Function, and Structure from Medical Images. SPIE 2008;6916:614–91.
    PubMed
  • 250 Armato SG 3rd, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, 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 Feb;38(2):915-31.
    PubMed
  • 251 LOLA11 Grand Challenge. Lobe and Lung Analysis 2011 (LOLA11); 2011. Available from: https://lola11.grand-challenge.org/ [cited 2022 Jan 30].
    PubMed
  • 252 LUNA16 Grand Challenge. Lung Nodule Analysis 2016; 2016. Available from: https://luna16.grand-challenge.org/. [cited 2022 Jan 30].
    PubMed
  • 253 Yan T. Ccap: A chest Ct Dataset. IEEE DataPort; 2020.
    PubMed
  • 254 MedSeg. COVID-19 CT segmentation dataset; 2020. Available from: http://medicalsegmentation.com/covid19/. [cited 2022 Jan 30].
    PubMed
  • 255 Morozov SP, Andreychenko AE, Pavlov NA, Vladzymyrskyy AV, Ledikhova NV, Gombolevskiy VA et al. MosMedData: Chest CT Scans with COVID-19 related findings dataset. Available from: https://arxiv.org/abs/2005.06465. [cited 2022 Jan 30].
    PubMed
  • 256 Jun M, Cheng G, Yixin W, Xingle A, Jiantao G, Ziqi Y, et al. COVID-19 CT Lung and Infection Segmentation Dataset (Verson 1.0) [Data set]. Zenodo. Available from: https://doi.org/10.5281/zenodo.3757476
    PubMed
  • 257 Simpson AL, Antonelli M, Bakas S, Bilello M, Farahani K, Van Ginneken B, et al. A large annotated medical image dataset for the development and evaluation of segmentation algorithms; 2019. arXiv preprint 2019, arXiv:1902.09063.
    PubMed
  • 258 Kumar A, Agarwala S, Dhara AK, Nandi D, Thakur SB, Bhadra AK, Sadhu A. Segmentation of lung field in HRCT images using U-net based fully convolutional networks. Med Image Underst Anal 2018; 894:84-93.
    PubMed
  • 259 Murphy K, van Ginneken B, Reinhardt JM, Kabus S, Ding K, Deng X, et al. Evaluation of registration methods on thoracic CT: the EMPIRE10 challenge. IEEE Trans Med Imaging 2011 Nov;30(11):1901-20.
    PubMed
  • 260 VESSEL12 Grand Challenge. Vessel segmentation in the lung 2012 (vessel12); 2012. Available from: https://vessel12.grand-challenge.org. [cited 2022 Jan 30].
    PubMed
  • 261 Langs G, Müller H, Menze B, Hanbury A. VISCERAL: Towards Large Data in Medical Imaging - Challenges and Directions Med Content Based Retr Clin Decis Support 2013:92-8.
    PubMed
  • 262 Kaggle Competition. Data Science Bowl 2017 (DSB); 2017. [Online]. Available from: https://www.kaggle.com/c/data-science-bowl-2017. [cited 2022 Jan 30].
    PubMed
  • 263 Kaggle Competition, Finding and Measuring Lungs in CT Data. 2017. [Online]. Available: https://www.kaggle.com/kmader/finding-lungs-in-ct-data. [Accessed on Jan 30, 2022].
    PubMed
  • 264 Lo P, van Ginneken B, Reinhardt JM, Yavarna T, de Jong PA, Irving B, et al. Extraction of airways from CT (EXACT’09). IEEE Trans Med Imaging 2012 Nov;31(11):2093-107.
    PubMed
  • 265 Dicente Cid Y, del Toro OA, Depeursinge A, Müller H. Efficient and fully automatic segmentation of the lungs in CT volumes. In: Proceedings of the VISCERAL Anatomy Grand Challenge at the 2015 IEEE ISBI. IEEE: 2015. p. 31-5.
    PubMed
  • 266 Soares E, Angelov P, Biaso S, Froes MH, Abe DK. SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification. medRxiv: 2020.
    PubMed
  • 267 Blake G. CT Images in COVID-19. 2021. [Online]. Available from: https://wiki.cancerimagingarchive.net/display/Public/CT+Images+in+COVID-19
    PubMed
  • 268 Taha AA, Hanbury A. Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging. 2015 Aug 12;15:29.
    PubMed
  • 269 Jirapatnakul AC, Mulman YD, Reeves AP, Yankelevitz DF, Henschke CI. Segmentation of juxtapleural pulmonary nodules using a robust surface estimate. Int J Biomed Imaging 2011;2011:632195.
    PubMed
  • 270 Baker M. 1,500 scientists lift the lid on reproducibility. Nature 2016 May 26;533(7604):452-4.
    PubMed
  • 271 Yeghiazaryan V, Voiculescu I. An Overview of Current Evaluation Methods Used in Medical Image Segmentation. Oxford, UK: 2015.
    PubMed
  • 272 Fan DP, Zhou T, Ji GP, Zhou Y, Chen G, Fu H, et al. Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images. IEEE Trans Med Imaging 2020 Aug;39(8):2626-37.
    PubMed
  • 273 Pulmonary Toolkit [Online]. Available from: https://github.com/tomdoel/pulmonarytoolkit. [cited 2022 Jan 30].
    PubMed
  • 274 3D Slicer, 3D Slicer image computing platform. [Online]. Available from: https://www.slicer.org/. [cited 2022 Jan 30].
    PubMed
  • 275 ITK-SNAP [Online]. Available from: http://www.itksnap.org/pmwiki/pmwiki.php. [cited 2022 Jan 30].
    PubMed