Skip to main content
Hauptrubriken
CME Facharzt-Training Webinare Zeitschriften Bücher e.Medpedia Podcast
Service
Jobbörse Info & Hilfe
Fachgebiete
Anästhesiologie Allgemeinmedizin Arbeitsmedizin Augenheilkunde Chirurgie Dermatologie Gynäkologie und Geburtshilfe HNO Innere Medizin Kardiologie Neurologie Onkologie und Hämatologie Orthopädie und Unfallchirurgie Pädiatrie Pathologie Psychiatrie Radiologie Rechtsmedizin Urologie Zahnmedizin
Themen
Typ-2-Diabetes und Adipositas Klimawandel und Gesundheit Neues aus dem Markt COVID-19 Praxis und Beruf Seltene Erkrankungen GOÄ & EBM Panorama
Sammlungen
Kasuistiken Algorithmen & Infografiken Blickdiagnosen Arthropedia FlapFinder (für Dermatochirurgen) Videos Kongressberichterstattung Medizin für Apothekerinnen und Apotheker Für Ärztinnen und Ärzte in Weiterbildung Für Medizinstudierende

Die Highlights vom Kongress des American College of Cardiology 2024

Im April diskutierten die Herz-Kreislauf-Spezialisten aus der ganzen Welt auf dem  Kongress des American College of Cardiology (ACC) u.a. neue Therapieansätze bei akutem Myokardinfarkt, koronarer Herzerkrankung, kardiogenem Schock oder Aortenklappenstenose. In unserem Kongress-Dossier haben wir für Sie Berichte über die „Late-Breaking Trials“ und andere wichtige Studien zusammengestellt. 
Erweiterte Suche
Anmelden
nach oben
Journal of Imaging Informatics in Medicine
Erschienen in: Journal of Digital Imaging 6/2018

18.06.2018

3D Segmentation Algorithms for Computerized Tomographic Imaging: a Systematic Literature Review

verfasst von: L. E. Carvalho, A. C. Sobieranski, A. von Wangenheim

Erschienen in: Journal of Imaging Informatics in Medicine | Ausgabe 6/2018

Einloggen, um Zugang zu erhalten

Abstract

This paper presents a systematic literature review concerning 3D segmentation algorithms for computerized tomographic imaging. This analysis covers articles published in the range 2006—March 2018 found in four scientific databases (Science Direct, IEEEXplore, ACM, and PubMed), using the methodology for systematic review proposed by Kitchenham. We present the analyzed segmentation methods categorized according to its application, algorithmic strategy, validation, and use of prior knowledge, as well as its general conceptual description. Additionally, we present a general overview, discussions, and further prospects for the 3D segmentation methods applied for tomographic images.
Literatur
1.
Gonzalez RC, Woods RE: Digital image processing, 2nd edition. Upper Saddle River: Prentice-Hall, Inc., 2006
2.
Ilea DE, Whelan PF: Image segmentation based on the integration of colour-texture descriptors- a review. Pattern Recogn 44(10-11):2479–2501, 2011. Semi-Supervised Learning for Visual Content Analysis and Understanding
3.
Ma Z, Tavares J, Jorge RN: A review on the current segmentation algorithms for medical images, 2009, pp 135–140, 01
4.
Ma Z, Tavares JMRS, Jorge RM, Mascarenhas TR: Natal a review of algorithms for medical image segmentation and their applications to the female pelvic cavity. Comput Methods Biomech Biomed Eng 13(2):235–46, 2010
5.
Jodas DS, Pereira AS, Tavares JMRS: A review of computational methods applied for identification and quantification of atherosclerotic plaques in images. Expert Syst Appl 46:1–14, 2016
6.
Kitchenham B: Procedures for Performing Systematic Reviews. Technical report Joint Technical Report TR/SE-0401, 2004
7.
National Health, Medical Research Council (Australia), and Nhmrc Staff. How to Review the Evidence: Systematic Identification and Review of the Scientific Literature. Handbook series on preparing clinical practice guidelines. National Health and Medical Research Council, 2000
8.
National Health, Medical Research Council (Australia), and Nhmrc Staff. How to Use the Evidence: Assessment and Application of Scientific Evidence. Handbook series on preparing clinical practice guidelines. National Health and Medical Research Council, 2000
9.
Cochrane Collaboration. Cochrane Reviewers Handbook. Version 4.2.1. National Health and Medical Research Council, 2003
10.
University of York. NHS Centre for Reviews and Dissemination. Undertaking systematic reviews of research on effectiveness: CRD’s guidance for those carrying out or commissioning reviews. CRD report. NHS Centre for Reviews and Dissemination, University of York, 2001
11.
Katz YH: Automatic pattern recognition of meteorological satellite cloud photography, 1964
12.
Joan SW: A survey of threshold selection techniques. Comput Graph Image Process 7(2):259–265, 1978
13.
Li Z, Lee K, Niemeijer M, Mullins RF, Sonka M, Abramoff MD: Automated segmentation of the choroid from clinical sd-oct. Investig Ophthalmol Vis Sci 53(12):7510–7519, 2012
14.
Javaid M, Javid M, Rehman MZU, Shah SIA: A novel approach to {CAD} system for the detection of lung nodules in {CT} images. Comput Methods Programs Biomed 135:125–139, 2016PubMed
15.
Armato SG, McLennan Gx, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Aberle DR, Henschke CI, Hoffman EA, 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 38(2):915–931, 2011PubMedPubMedCentral
16.
Chang KY, Wu YH, Lin WL, Chen SJ, Chen LS: Vessel segmentation based on bone-to-bone elimination in brain ct angiography. In 2016 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), 2016, pp 1–2
17.
Migliori S, Chiastra C, Bologna M, Montin E, Dubini G, Aurigemma C, Fedele R, Burzotta F, Mainardi L, Migliavacca F: A framework for computational fluid dynamic analyses of patient-specific stented coronary arteries from optical coherence tomography images. Med Eng Phys 47:105–116, 2017PubMed
18.
Farzaneh N, Habbo-Gavin S, Soroushmehr SMR, Patel H, Fessell DP, Ward KR, Najarian K: Atlas based 3d liver segmentation using adaptive thresholding and superpixel approaches. In: 2017 IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP), 2017, pp 1093–1097
19.
Boykov Y, Veksler O, Zabih R: Markov random fields with efficient approximations. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR ’98. IEEE Computer Society, Washington, 1998, p 648
20.
Yi F, Moon I: Image segmentation: a survey of graph-cut methods. In: 2012 International Conference on Systems and Informatics (ICSAI), 2012, pp 1936–1941
21.
Wieclawek W, Pietka E: Live-wire-based 3d segmentation method. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007, pp 5645–5648
22.
Liu J, Udupa JK, Saha PK, Odhner D, Hirsch BE, Siegler S, Simon S, Winkelstein BA: Rigid model-based 3d segmentation of the bones of joints in mr and ct images for motion analysis. Med Phys 35(8):3637–3649, 2008PubMedPubMedCentral
23.
Aslan MS, Ali A, Arnold B, Fahmi R, Farag AA, Xiang P: Segmentation of trabecular bones from vertebral bodies in volumetric ct spine images. In: 2009 16th IEEE International Conference on Image Processing (ICIP), 2009, pp 3385–3388
24.
Aslan MS, Ali A, Rara H, Arnold B, Fahmi R, Farag AA, Xiang P: A novel, fast, and complete 3d segmentation of vertebral bones. In: 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010, pp 654–657
25.
Lu K, Higgins WE: Segmentation of the central-chest lymph nodes in 3d {MDCT} images. Comput Biol Med 41(9):780–789, 2011PubMedPubMedCentral
26.
Liu X, Tuncali K, Wells WM, Morrison PR, Zientara GP: Fully automatic 3d segmentation of iceball for image-guided cryoablation. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012, pp 2327–2330
27.
Boykov Y, Kolmogorov V: An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision. IEEE Trans Pattern Anal Mach Intell 26(9):1124–1137, 2004PubMed
28.
Beichel R, Bornik A, Bauer C, Sorantin E: Liver segmentation in contrast enhanced ct data using graph cuts and interactive 3d segmentation refinement methods. Med Phys 39(3):1361–1373, 2012PubMedPubMedCentral
29.
Beichel RR, Wang Y: Computer-aided lymph node segmentation in volumetric ct data. Med Phys 39(9):5419–5428, 2012PubMedPubMedCentral
30.
Chen X, Niemeijer M, Zhang L, Lee K, Abramoff MD, Sonka M: 3D segmentation of fluid-associated abnormalities in retinal oct Probability constrained graph-search-graph-cut. IEEE Trans Med Imaging 31(8):1521–1531, 2012PubMedPubMedCentral
31.
Pazokifard B, Sowmya A: 3-d segmentation of human sternum in lung mdct images. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013, pp 3351–3354
32.
Grosgeorge D, Petitjean C, Dubray B, Su R: Esophagus segmentation from 3d ct data using skeleton prior-based graph cut. Computational and mathematical methods in medicine, 2013
33.
El-Zehiry N, Jolly MP, Sofka M: A splice-guided data driven interactive editing. In: 2013 IEEE 10th International Symposium on Biomedical Imaging, 2013, pp 1098–1101
34.
Antony BJ, Miri MS, Abràmoff MD, Kwon YH, Garvin MK, Howe R: Automated 3d segmentation of multiple surfaces with a shared hole: Segmentation of the neural canal opening in sd-oct volumes. In: (Golland P, Hata N, Barillot C, Hornegger J, Eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. Springer International Publishing, Cham, 2014, pp 739–746
35.
Jodoin PM, Pinheiro F, Oudot A, Lalande A: Left-ventricle segmentation of spect images of rats. IEEE Trans Biomed Eng 62(9):2260–2268, 2015PubMed
36.
Vasquez D, Scharcanski J, Wong A: Automatic framework for extraction and characterization of wetting front propagation using tomographic image sequences of water infiltrated soils. PLOS ONE 01(1):1–12, 2015
37.
Kitrungrotsakul T, Chen Y-W, Han X-H, Lin L: Supervoxels based graph cut for medical organ segmentation. IFAC-PapersOnLine 48(20):70–75, 2015. 9th {IFAC} Symposium on Biological and Medical Systems {BMS} 2015Berlin, Germany, 31 August-2 September 2015
38.
Gangsei LE, Kongsro J: Automatic segmentation of computed tomography (ct) images of domestic pig skeleton using a 3d expansion of dijkstra’s algorithm. Comput Electron Agric 121:191–194, 2016
39.
Cha JW, Farhangi MM, Dunlap N, Amini A: Volumetric analysis of respiratory gated whole lung and liver ct data with motion-constrained graph cuts segmentation. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017, pp 3405–3408
40.
Osher S, Sethian JA: Fronts propagating with curvature dependent speed: algorithms based on hamilton-jacobi formulations. J Comput Phys 79(1):12–49, 1988
41.
Burger M, Osher SJ: A survey on level set methods for inverse problems and optimal design, 2004
42.
Liu J-W, Feng H-Q, Zhou Y-Y, LI C-F: A novel automatic extraction method of lung texture tree from {HRCT} images. Acta Autom Sin 35(4):345–349, 2009
43.
Chen D, Fahmi R, Farag AA, Falk RL, Dryden, GW: Accurate and fast 3d colon segmentation in ct colonography. In: 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009, pp 490–493
44.
Chen D, Farag AA, Falk RL, Dryden, GW: A variational framework for 3d colonic polyp visualization in virtual colonoscopy. In: 2009 16th IEEE International Conference on Image Processing (ICIP), 2009, pp 2617–2620
45.
Hadjiiski L, Mukherji SK, Ibrahim M, Sahiner B, Gujar SK, Moyer J, Chan H-P: Head and neck cancers on ct: preliminary study of treatment response assessment based on computerized volume analysis. Amer J Roentgenol 194(4):1083–1089, 2010
46.
Hadjiiski L, Mukherji SK, Gujar SK, Sahiner B, Ibrahim M, Street E, Moyer J, Worden FP, Chan H-P: Treatment response assessment of head and neck cancers on ct using computerized volume analysis. Amer J Neuroradiol 31(9):1744–1751, 2010
47.
Hadjiiski L, Weizer AZ, Alva A, Caoili EM, Cohan RH, Cha K, Chan H-P: Treatment response assessment for bladder cancer on ct based on computerized volume analysis, world health organization criteria, and recist. Amer J Roentgenol 205(2):348–352, 2015
48.
Hutter J, Hofmann HG, Grimm R, Greiser A, Saake M, Hornegger J, Dörfler A, Schmitt P: Prior-based automatic segmentation of the carotid artery lumen in tof mra (pascal) Berlin: Springer, 2012, pp 511–518
49.
Qi Y, Dong K, Yin L, Li M: 3d segmentation of the lung based on the neighbor information and curvature. In: Proceedings of the 2013 Seventh International Conference on Image and Graphics, ICIG ’13. IEEE Computer Society, Washington, 2013, pp 139–143
50.
Hemmati H, Kamli-Asl A, Talebpour A, Shirani S: Semi-automatic 3d segmentation of carotid lumen in contrast-enhanced computed tomography angiography images. Phys Med 31(8):1098–1104, 2015PubMed
51.
Badura P, Wieclawek W: Calibrating level set approach by granular computing in computed tomography abdominal organs segmentation. Appl Soft Comput 49(C):887–900, 2016
52.
53.
Suetens P, Verbeeck R, Delaere D, Nuyts J, Bijnens (Auth.) B, Stefanelli M, Hasman A, Fieschi M, Talmon J: AIME 91: Proceedings of the Third Conference on Artificial Intelligence in Medicine, Maastricht, June 24-27, Lecture Notes in Medical Informatics, vol 44, 1st edition. Berlin: Springer, 1991
54.
Geman S, Geman D: Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell PAMI-6(6):721–741, 1984
55.
Huang, Rui, Pavlovic V, Metaxas DN: A tightly coupled region-shape framework for 3d medical image segmentation. In: 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006, pp 426–429
56.
Juslin A, Tohka, J: Unsupervised segmentation of cardiac pet transmission images for automatic heart volume extraction. In: 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, 2006, pp 1077–1080
57.
Müller O, Donner S, Klinder T, Dragon R, Bartsch I, Witte F, Krüger A, Heisterkamp A, Rosenhahn B: Model based 3d segmentation and oct image undistortion of percutaneous implants. In: Proceedings of the 14th International Conference on Medical Image Computing and Computer-assisted Intervention - Volume Part III, MICCAI’11. Springer, Berlin, 2011, pp 454–462
58.
Bhole C, Morsillo N, Pal C: 3d segmentation in ct imagery with conditional random fields and histograms of oriented gradients. In: Proceedings of the Second International Conference on Machine Learning in Medical Imaging, MLMI’11. Springer, Berlin, 2011, pp 326–334
59.
Mesejo P, Ibánez Ó, Cordón Ó, Cagnoni S: A survey on image segmentation using metaheuristic-based deformable models: state of the art and critical analysis. Appl Soft Comput 44:1–29, 2016
60.
Terzopoulos D, Fleischer K: Deformable models. Vis Comput 4(6):306–331, 1988. cited By 392
61.
Saragaglia A, Fetita C, Prêteux F: Assessment of airway remodeling in asthma: Volumetric versus surface quantification approaches. In: Proceedings of the 9th International Conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II, MICCAI’06. Springer, Berlin, 2006, pp 413–420
62.
Zhang H, Yang L, Foran DJ, Nosher JL, Yim PJ: 3d segmentation of the liver using free-form deformation based on boosting and deformation gradients. In: Proceedings of the Sixth IEEE International Conference on Symposium on Biomedical Imaging: From Nano to Macro, ISBI’09. IEEE Press, Piscataway, 2009, pp 494–497
63.
Ding, F, Yang W, Leow WK, Venkatesh SK: 3d segmentation of soft organs by flipping-free mesh deformation. In: 2009 Workshop on Applications of Computer Vision (WACV), 2009, pp 1–7
64.
Cascio D, Magro R, Fauci F, Iacomi M, Raso G: Automatic detection of lung nodules in ct datasets based on stable 3d mass-spring models. Comput Biol Med 42(11):1098–1109, 2012PubMed
65.
Delibasis KK, Christodoulidis A, Maglogiannis I: An intelligent tool for anatomical object segmentation using deformable surfaces. In: Proceedings of the 7th Hellenic Conference on Artificial Intelligence: Theories and Applications, SETN’12. Springer, Berlin, 2012, pp 206–213
66.
Shi C, Guo C, Cheng Y, Wang J: Greedy algorithm based deformable simplex meshes using gradient vector flow as external energy. In: 2014 7th International Conference on Biomedical Engineering and Informatics, 2014, pp 199–204
67.
Lu D, Wu Y, Harris G, Cai W: Iterative mesh transformation for 3d segmentation of livers with cancers in ct images. Comput Med Imaging Graph 43:1–14, 2015PubMedPubMedCentral
68.
Kass M, Witkin A, Terzopoulos D: Snakes: active contour models. Int J Comput Vis 1(4):321–331, 1988
69.
Jiang H, Cheng Q: Automatic 3d segmentation of ct images based on active contour models. In: 11th IEEE International Conference on Computer-Aided Design and Computer Graphics, 2009. CAD/graphics ’09, 2009, pp 540–543
70.
Barbosa D, Dietenbeck T, Schaerer J, D’hooge J, Friboulet D, Bernard O: B-spline explicit surfaces: active an efficient framework for real-time 3-d region-based segmentation. IEEE Trans Image Process 21(1):241–251, 2012PubMed
71.
Qi D, Angelini ED, Laine AF: Real-time segmentation by active geometric functions. Comput Methods Prog Biomed 98(3):223–230, 2010
72.
Urschler M, Bornik A, Scheurer E, Yen K, Bischof H, Schmalstieg D: Forensic-case analysis: from 3d imaging to interactive visualization. IEEE Comput Graph Appl 32(4):79–87, 2012PubMed
73.
Mezlini H, Youssef R, Bouhadoun H, Budyn E, Denis Laredo J, Ghalila SS, Chappard C: High resolution volume quantification of the knee joint space based on a semi-automatic segmentation of computed tomography images. In: 2015 International Conference on Systems, Signals and Image Processing (IWSSIP), 2015, pp 157–161
74.
Akkus Z, Sedlar J, Coufalova L, Korfiatis P, Kline TL, Warner JD, Agrawal J, Erickson BJ: Semi-automated segmentation of pre-operative low grade gliomas in magnetic resonance imaging. Cancer Imaging 15(1):12, 2015PubMedPubMedCentral
75.
Heimann T, Meinzer H-P: Statistical shape models for 3d medical image segmentation A review. Med Image Anal 13(4):543–563, 2009PubMed
76.
Cootes TF, Taylor CJ, Cooper DH, Graham J: Training Models of Shape from Sets of Examples London: Springer, 1992, pp 9–18
77.
Nain D, Haker S, Bobick A, Tannenbaum A Shape-driven 3d segmentation using spherical wavelets. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 2006, pp 66–74
78.
Moussavi F, Heitz G, Amat F, Comolli LR, Koller D, Horowitz M: 3d segmentation of cell boundaries from whole cell cryogenic electron tomography volumes. J Struct Biol 170(1):134–145, 2010PubMed
79.
Badakhshannoory H, Saeedi P: A model-based validation scheme for organ segmentation in ct scan volumes. IEEE Trans Biomed Eng 58(9):2681–2693, 2011PubMed
80.
Zhang S: Towards Robust and Effective Shape Prior Modeling: Sparse Shape Composition. New Brunswick: PhD thesis 2012, AAI3502515
81.
Zhang S, Huang J, Uzunbas M, Shen T, Delis F, Huang X, Volkow N, Thanos P, Metaxas DN: 3d segmentation of rodent brain structures using hierarchical shape priors and deformable models. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 2011, pp 611–618
82.
Rüegsegger MB, Cuadra Meritxell B, Pica A, Amstutz CA, Rudolph T, Aebersold D, Kowal JH: Statistical modeling of the eye for multimodal treatment planning for external beam radiation therapy of intraocular tumors. Int J Radiat Oncol*Biol*Phys 84(4):e541–e547, 2012PubMed
83.
Chang Y-B, Xia JJ, Yuan P, Kuo T-H, Xiong Z, Gateno J, Zhou X: 3d segmentation of maxilla in cone-beam computed tomography imaging using base invariant wavelet active shape model on customized two-manifold topology. J X-ray Sci Technol 21(2):251–282, 2013
84.
Brice CR, Fennema CL: Scene analysis using regions. Artif Intell 1(3):205–226, 1970
85.
Haralick RM, Shapiro LG: Image segmentation techniques. Comput Vis Graph Image Process 29(1):100–132, 1985
86.
Freixenet J, Muñoz X, Raba D, Martí J, Cufí X: Yet another survey on image segmentation: Region and boundary information integration. In: Proceedings of the 7th European Conference on Computer Vision-Part III, ECCV ’02. Springer, London, 2002, pp 408–422
87.
Bernard Davis J, Reiner B, Huser M, Burger C, Székely G, Frank Ciernik I: Assessment of 18f {PET} signals for automatic target volume definition in radiotherapy treatment planning. Radiother Oncol 80(1):43–50, 2006PubMed
88.
Staal J, van Ginneken B, Viergever MA: Automatic rib segmentation and labeling in computed tomography scans using a general framework for detection, recognition and segmentation of objects in volumetric data. Med Image Anal 11(1):35–46, 2007PubMed
89.
Monga O: Defining and computing stable representations of volume shapes from discrete trace using volume primitives: Application to 3d image analysis in soil science. Image Vis Comput 25(7):1134–1153, 2007
90.
Bulu H, Alpkocak A: Comparison of 3d segmentation algorithms for medical imaging. In: Twentieth IEEE International Symposium on Computer-Based Medical Systems (CBMS’07), June 2007, pp 269–274
91.
Diciotti S, Picozzi G, Falchini M, Mascalchi M, Villari N, Valli G: 3-d segmentation algorithm of small lung nodules in spiral ct images. IEEE Trans Inf Technol Biomed 12(1):7–19, 2008PubMed
92.
Armato SG, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Aberle DR, Henschke CI, Hoffman EA, Kazerooni EA, MacMahon H, Beek EJR, Yankelevitz D, Biancardi AM, Bland PH, Brown MS, Engelmann RM, Laderach GE, Max D, Pais RC, Qing DP-Y, Roberts RY, Smith AR, Starkey A, Batra P, Caligiuri P, Farooqi A, Gladish GW, Matilda Jude C, Munden RF, Petkovska I, Quint LE, Schwartz LH, Sundaram B, Dodd LE, Fenimore C, Gur D, Petrick N, Freymann J, Kirby J, Hughes B, Casteele AV, Gupte S, Sallam M, Heath MD, Kuhn MH, Dharaiya E, Burns R, Fryd DS, Salganicoff M, Anand V, Shreter U, Vastagh S, Croft BY, Clarke LP: The lung image database consortium (lidc) and image database resource initiative (idri): a completed reference database of lung nodules on ct scans. Med Phys 38(2):915–931 , 2011PubMedPubMedCentral
93.
Lai K, Zhao P, Huang Y, Liu J, Wang C, Feng H, Li C: Automatic 3d segmentation of lung airway tree A novel adaptive region growing approach. In: 2009 3rd International Conference on Bioinformatics and Biomedical Engineering, 2009, pp 1–4
94.
De Nunzio G, Tommasi E, Agrusti A, Cataldo R, De Mitri I, Favetta M, Maglio S, Massafra A, Quarta M, Torsello M, Zecca I, Bellotti R, Tangaro S, Calvini P, Camarlinghi N, Falaschi F, Cerello P, Oliva P: Automatic lung segmentation in ct images with accurate handling of the hilar region. J Digit Imaging 24(1):11–27, 2011PubMed
95.
Bert A, Dmitriev I, Agliozzo S, Pietrosemoli N, Mandelkern M, Gallo T, Regge D: An automatic method for colon segmentation in {CT} colonography. Comput Med Imaging Graph 33(4):325–331, 2009PubMed
96.
Gloger O, Kühn J, Stanski A, Völzke H, Puls R: A fully automatic three-step liver segmentation method on lda-based probability maps for multiple contrast {MR} images. Magn Reson Imaging 28(6):882–897, 2010PubMed
97.
Ren Yh, Sun Xw, Nie Sd: A 3d segmentation method of lung parenchyma based on ct image sequences. In: International Conference on Information, Networking and Automation (ICINA), vol 2, 2010, pp V2–332–V2–336
98.
Uher V, Burget R: Automatic 3d segmentation of human brain images using data-mining techniques. In: 2012 35th International Conference on Telecommunications and Signal Processing (TSP), 2012, pp 578–580
99.
Andrä H, Combaret N, Dvorkin J, Glatt E, Han J, Kabel M, Keehm Y, Krzikalla F, Lee M, Madonna C, Marsh M, Mukerji T, Saenger EH, Sain R, Saxena N, Ricker S, Wiegmann A, Zhan X: Digital rock physics benchmarks-part i: Imaging and segmentation. Comput Geosci 50:25–32, 2013. Benchmark problems, datasets and methodologies for the computational geosciences
100.
Badura P, Pietka E: Soft computing approach to 3d lung nodule segmentation in {CT}. Comput Biol Med 53: 230–243, 2014PubMed
101.
Werz T, Baumann M, Wolfram U, Krill III CE: Particle tracking during ostwald ripening using time-resolved laboratory x-ray microtomography. Mater Charact 90:185–195, 2014
102.
Paulano F, Jiménez JJ, Pulido R: 3d segmentation and labeling of fractured bone from ct images. Vis Comput 30(6-8):939–948, 2014
103.
Pal NR, Pal SK: A review on image segmentation techniques. Pattern Recogn 26 (9): 1277–1294, 1993
104.
Blanz WE, Gish SL: A connectionist classifier architecture applied to image segmentation. In: [1990] Proceedings, 10th International Conference on Pattern Recognition, volume ii, 1990, pp 272–277
105.
Amza, C: A review on neural network–based image segmentation techniques. De Montfort University, Mechanical and Manufacturing Engg, The gateway leicester, LE1 9BH, United Kingdom, 2012, pp 1–23
106.
Li S, Fevens T, Krzyźak A, Li S: Automatic clinical image segmentation using pathological modeling, {PCA} and {SVM}. Eng Appl Artif Intell 19(4):403–410, 2006. Recent Advances in Data Mining
107.
Chang Q, Shi J, Xiao Z: A new 3d segmentation algorithm based on 3d pcnn for lung ct slices. In: 2009 2nd International Conference on Biomedical Engineering and Informatics, 2009, pp 1–5
108.
Santos AM, de Carvalho Filho AO, Silva AC, de Paiva AC, Nunes RA, Gattass M: Automatic detection of small lung nodules in 3d {CT} data using gaussian mixture models, tsallis entropy and {SVM}. Eng Appl Artif Intell 36: 27–39, 2014
109.
Ye ZZ, Yu QZ, Liao M, Zou BJ, Wang XF, Wang W: Liver vessel segmentation based on extreme learning machine. Phys Med 32(5):709–716, 2016
110.
Wang S, Mu Z, Liu Z, Liu Z, Gu D, Zang Y, Di D, Gevaert O, Tian J: Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation. Med Image Anal 40:172–183, 2017PubMedPubMedCentral
111.
Gibson E, Li W, Sudre C, Fidon L, Shakir DI, Wang G, Eaton-Rosen Z, Gray R, Doel T, Hu Y, Whyntie T, Nachev P, Modat M, Barratt DC, Ourselin S, Jorge Cardoso M, Vercauteren T: Niftynet: a deep-learning platform for medical imaging. Comput Methods Programs Biomed 158:113–122, 2018PubMedPubMedCentral
112.
Chaves R, Ramírez J, Górriz JM: Integrating discretization and association rule-based classification for alzheimer’s disease diagnosis. Expert Syst Appl 40 (5): 1571–1578, 2013
113.
Spampinato C, Pino C, Giordano D, Leonardi R: Automatic 3d segmentation of mandible for assessment of facial asymmetry. In: 2012 IEEE International Symposium on Medical Measurements and Applications Proceedings (MeMeA), 2012, pp 1–4
114.
Karvonen T, Uranishi Y, Sakamoto T, Tona Y, Okamoto K, Tamura H, Kuroda T: 3d reconstruction of cochlea using optical coherence tomography. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2016, pp 5905–5908
115.
Wang Q, Song E, Jin R, Han P, Wang X, Zhou Y, Zeng J: Segmentation of lung nodules in computed tomography images using dynamic programming and multidirection fusion techniques1. Acad Radiol 16(6):678–688, 2009PubMed
116.
Lloréns R, Naranjo V, López F, Alcaniz M: Jaw tissues segmentation in dental 3d {CT} images using fuzzy-connectedness and morphological processing. Comput Methods Programs Biomed 108(2):832–843, 2012PubMed
117.
Xu Z, Bagci U, Jonsson C, Jain S, Mollura DJ: Efficient ribcage segmentation from ct scans using shape features. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014, pp 2899–2902
118.
Song H, Kang W, Zhang Q, Wang S: Kidney segmentation in ct sequences using skfcm and improved growcut algorithm. BMC Syst Biol 9:1–11, 2015
119.
Zhang W, Kim HM: Fully automatic colon segmentation in computed tomography colonography. In: 2016 IEEE International Conference on Signal and Image Processing (ICSIP), 2016, pp 51–55
120.
Jang Y, Ho YJ, Hong Y, Cho I, Shim H, Chang H: Geodesic distance algorithm for extracting the ascending aorta from 3d CT images. Comp Math Methods Med 2016:4561979:1–4561979:7, 2016
121.
Rusu M, Starosolski Z, Wahle M, Rigort A, Wriggers W: Automated tracing of filaments in 3d electron tomography reconstructions using sculptor and situs. J Struct Biol 178(2):121–128, 2012. Special Issue: Electron TomographyPubMedPubMedCentral
122.
Gonçalves L, Novo J, Campilho A: Hessian based approaches for 3d lung nodule segmentation. Expert Syst Appl 61:1–15, 2016
123.
Fabijańska A, Goclawski J: 3d segmentation of the cerebrospinal fluid from ct brain scans using local histogram similarity map. In: 2015 IEEE 2nd International Conference on Cybernetics (CYBCONF), 2015, pp 113–118
124.
Wan X, Yang F, Yang F, Li X, Xu M, Tian J: Visualization of multiple anatomical structures with explicit isosurface manipulation. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015, pp 4234–4237
125.
Biesdorf A, Wörz S, von Tengg-Kobligk H, Rohr K, Schnörr C: 3d segmentation of vessels by incremental implicit polynomial fitting and convex optimization. In: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), 2015, pp 1540–1543
126.
Farzaneh N, Soroushmehr SMR, Williamson CA, Jiang C, Srinivasan A, Bapuraj JR, Ward KR, Korley FK, Najarian K: Automated subdural hematoma segmentation for traumatic brain injured (tbi) patients. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017, pp 3069–3072
127.
Czabaj MW, Riccio ML, Whitacre WW: Numerical reconstruction of graphite/epoxy composite microstructure based on sub-micron resolution x-ray computed tomography. Compos Sci Technol 105: 174–182, 2014
128.
Loss LA, Bebis G, Chang H, Auer M, Sarkar P, Parvin B: Automatic segmentation and quantification of filamentous structures in electron tomography New York: ACM, 2012, pp 170–177
129.
Badura P: Virtual bacterium colony in 3d image segmentation. Comput Med Imaging Graph 65: 152–166, 2018. Advances in Biomedical Image ProcessingPubMed
130.
Ontiveros S, Yagüe JA, Jiménez R, Brosed F: Computer tomography 3d edge detection comparative for metrology applications. Procedia Eng 63:710–719, 2013. The Manufacturing Engineering Society International Conference {MESIC}
131.
Rudyanto RD, Kerkstra S, van Rikxoort EM, Fetita C, Brillet P-Y, Lefevre C, Xue W, Zhu X, Liang J, Öksüz Í, Ünay D, Kadipasaogˇlu K, San josé estépar R, Ross JC, Washko GR, Prieto J-C, Hoyos MH, Orkisz M, Meine H, Hüllebrand M, Stöcker C, Mir Fernando L, Naranjo V, Villanueva E, Staring M, Xiao C, Stoel BC, Fabijanska Anna, Smistad Erik, Elster AC, Lindseth F, Foruzan AH, Kiros R, Popuri K, Cobzas D, Jimenez-Carretero D, Santos A, Ledesma-Carbayo MJ, Helmberger M, Urschler M, Pienn M, Bosboom DGH, Campo A, Prokop M, de Jong Pim A, de Solorzano CO, Barrutia AM, van Ginneken B: Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the {VESSEL12} study. Med Image Anal 18(7):1217–1232, 2014PubMedPubMedCentral
132.
Valente IRS, Cortez PC, Neto EC, Soares JM, de Albuquerque VHC, Tavares JMRS: Automatic 3d pulmonary nodule detection in ct images. Comput Methods Prog Biomed 124(C):91–107, 2016
133.
Dice LR: Measures of the amount of ecologic association between species. Ecology 26(3):297–302, 1945
134.
Ben-Hur A, Elisseeff A, Guyon I: A stability based method for discovering structure in clustered data Pacific Symposium on Biocomputing Pacific Symposium on Biocomputing, 2002, pp 6–17
135.
Chang H-H, Zhuang AH, Valentino DJ, Chu W-C: Performance measure characterization for evaluating neuroimage segmentation algorithms. NeuroImage 47(1):122–135, 2009PubMed
136.
Taha AA, Hanbury A: Metrics for evaluating 3d medical image segmentation: analysis, selection, and tool. BMC Med Imaging 15(1):29, 2015PubMedPubMedCentral
Metadaten
Titel
3D Segmentation Algorithms for Computerized Tomographic Imaging: a Systematic Literature Review
verfasst von
L. E. Carvalho
A. C. Sobieranski
A. von Wangenheim
Publikationsdatum
18.06.2018
Verlag
Springer International Publishing
Erschienen in
Journal of Imaging Informatics in Medicine / Ausgabe 6/2018
Print ISSN: 2948-2925
Elektronische ISSN: 2948-2933
DOI
https://doi.org/10.1007/s10278-018-0101-z

Weitere Artikel der Ausgabe 6/2018

Journal of Digital Imaging 6/2018 Zur Ausgabe

OriginalPaper

Combination of rs-fMRI and sMRI Data to Discriminate Autism Spectrum Disorders in Young Children Using Deep Belief Network

OriginalPaper

Of Mice and Roentgen: Radiologist Satisfaction with a Non-conventional 13-Button Mouse—One Institution’s Experience

OriginalPaper

Axillary Lymph Node Evaluation Utilizing Convolutional Neural Networks Using MRI Dataset

OriginalPaper

An Unsupervised Approach for Extraction of Blood Vessels from Fundus Images

OriginalPaper

Reengineering Workflow for Curation of DICOM Datasets

OriginalPaper

A Decision-Support Tool for Renal Mass Classification

Neu im Fachgebiet Radiologie

Darf man die Behandlung eines Neonazis ablehnen?

08.05.2024 Gesellschaft Nachrichten

In einer Leseranfrage in der Zeitschrift Journal of the American Academy of Dermatology möchte ein anonymer Dermatologe bzw. eine anonyme Dermatologin wissen, ob er oder sie einen Patienten behandeln muss, der eine rassistische Tätowierung trägt.

Ein Drittel der jungen Ärztinnen und Ärzte erwägt abzuwandern

07.05.2024 Klinik aktuell Nachrichten

Extreme Arbeitsverdichtung und kaum Supervision: Dr. Andrea Martini, Sprecherin des Bündnisses Junge Ärztinnen und Ärzte (BJÄ) über den Frust des ärztlichen Nachwuchses und die Vorteile des Rucksack-Modells.

Endlich: Zi zeigt, mit welchen PVS Praxen zufrieden sind

IT für Ärzte Nachrichten

Darauf haben viele Praxen gewartet: Das Zi hat eine Liste von Praxisverwaltungssystemen veröffentlicht, die von Nutzern positiv bewertet werden. Eine gute Grundlage für wechselwillige Ärztinnen und Psychotherapeuten.

Akuter Schwindel: Wann lohnt sich eine MRT?

28.04.2024 Schwindel Nachrichten

Akuter Schwindel stellt oft eine diagnostische Herausforderung dar. Wie nützlich dabei eine MRT ist, hat eine Studie aus Finnland untersucht. Immerhin einer von sechs Patienten wurde mit akutem ischämischem Schlaganfall diagnostiziert.

Update Radiologie

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.

Newsletter bestellen
Bildnachweise