INFORMAZIONI SU QUESTO ARTICOLO

Cita

1 Neumann E, Schaefer-Ridder M, Wang Y, Hofschneider PH. Gene transfer into mouse lyoma cells by electroporation in high electric fields. EMBO J 1982; 1: 841-5. 10.1002/j.1460-2075.1982.tb01257.xSearch in Google Scholar

2 Kotnik T, Kramar P, Pucihar G, Miklavcic D, Tarek M. Cell membrane electroporation- Part 1: The phenomenon. IEEE Electr Insul Mag 2012; 28: 14-23. 10.1109/MEI.2012.6268438Search in Google Scholar

3 Mir LM, Orlowski S, Belehradek J, Paoletti C. Electrochemotherapy potentiation of antitumour effect of bleomycin by local electric pulses. Eur J Cancer 1991; 27: 68-72. 10.1016/0277-5379(91)90064-KSearch in Google Scholar

4 Sersa G, Miklavcic D, Cemazar M, Rudolf Z, Pucihar G, Snoj M. Electrochemotherapy in treatment of tumours. Eur J Surg Oncol 2008; 34: 232-40. 10.1016/j.ejso.2007.05.016Search in Google Scholar

5 Mali B, Jarm T, Snoj M, Sersa G, Miklavcic D. Antitumor effectiveness of electrochemotherapy: a systematic review and meta-analysis. Eur J Surg Oncol 2013; 39: 4-16. 10.1016/j.ejso.2012.08.016Search in Google Scholar

6 Lacković I, Magjarević R, Miklavčič D. Three-dimensional finite-element analysis of joule heating in electrochemotherapy and in vivo gene electrotransfer. IEEE Trans Dielectr Electr Insul 2009; 16: 1338-47. 10.1109/TDEI.2009.5293947Search in Google Scholar

7 Davalos R V., Mir LM, Rubinsky B. Tissue Ablation with Irreversible Electroporation. Ann Biomed Eng 2005; 33: 223-31. 10.1007/s10439-005-8981-8Search in Google Scholar

8 Chu KF, Dupuy DE. Thermal ablation of tumours: biological mechanisms and advances in therapy. Nat Rev Cancer 2014; 14: 199-208. 10.1038/nrc3672Search in Google Scholar

9 Kos B, Zupanic A, Kotnik T, Snoj M, Sersa G, Miklavcic D. Robustness of treatment planning for electrochemotherapy of deep-seated tumors. J Membr Biol 2010; 236: 147-53. 10.1007/s00232-010-9274-1Search in Google Scholar

10 Miklavcic D, Beravs K, Semrov D, Cemazar M, Demsar F, Sersa G. The importance of electric field distribution for effective in vivo electroporation of tissues. Biophys J 1998; 74: 2152-8. 10.1016/S0006-3495(98)77924-XSearch in Google Scholar

11 Mali B, Miklavcic D, Campana LG, Cemazar M, Sersa G, Snoj M, et al. Tumor size and effectiveness of electrochemotherapy. Radiol Oncol 2013; 47: 32-41. Search in Google Scholar

12 Miklavcic D, Snoj M, Zupanic A, Kos B, Cemazar M, Kropivnik M, et al. Towards treatment planning and treatment of deep-seated solid tumors by electrochemotherapy. Biomed Eng Online 2010; 9: 10. 10.1186/1475-925X-9-10284368420178589Search in Google Scholar

13 Pavliha D, Kos B, Zupanič A, Marčan M, Serša G, Miklavčič D. Patient-specific treatment planning of electrochemotherapy: Procedure design and possible pitfalls. Bioelectrochemistry 2012; 87: 265-73. 10.1016/j.bioelechem.2012.01.00722341626Search in Google Scholar

14 Kos B, Zupanic A, Kotnik T, Snoj M, Sersa G, Miklavcic D. Robustness of treatment planning for electrochemotherapy of deep-seated tumors. J Membr Biol 2010; 236: 147-53. 10.1007/s00232-010-9274-120596859Search in Google Scholar

15 Županič A, Čorović S, Miklavčič D. Optimization of electrode position and electric pulse amplitude in electrochemotherapy. Radiol Oncol 2008; 42: 93-101. 10.2478/v10019-008-0005-5Search in Google Scholar

16 Edhemovic I, Gadzijev EM, Brecelj E, Miklavcic D, Kos B, Zupanic A, et al. Electrochemotherapy: a new technological approach in treatment of metastases in the liver. Technol Cancer Res Treat 2011; 10: 475-85. 10.7785/tcrt.2012.500224452741421895032Search in Google Scholar

17 Pavliha D, Mušič MM, Serša G, Miklavčič D. Electroporation-based treatment planning for deep-seated tumors based on automatic liver segmentation of MRI images. PLoS One 2013; 8: e69068. 10.1371/journal.pone.0069068373227523936315Search in Google Scholar

18 Fraass B, Doppke K, Hunt M, Kutcher G, Starkschall G, Stern R, et al. American Association of Physicists in Medicine Radiation Therapy Committee Task Group 53: quality assurance for clinical radiotherapy treatment planning. Med Phys 1998; 25: 1773-829. 10.1118/1.5983739800687Search in Google Scholar

19 Payne S, Flanagan R, Pollari M, Alhonnoro T, Bost C, O’Neill D, et al. Imagebased multi-scale modelling and validation of radio-frequency ablation in liver tumours. Philos Trans A Math Phys Eng Sci 2011; 369: 4233-54. Search in Google Scholar

20 Alhonnoro T, Pollari M, Lilja M, Flanagan R, Kainz B, Muehl J, et al. Vessel Segmentation for Ablation Treatment Planning and Simulation. In: Jiang T, Navab N, Pluim JPW, et al., editors. Medical image computing and computer- assisted intervention : MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention. Volume 6361. Berlin, Heidelberg: Springer; 2010. p. 45-52. 10.1007/978-3-642-15705-9_620879213Search in Google Scholar

21 Hansen PD, Rogers S, Corless CL, Swanstrom LL, Siperstien AE. Radiofrequency ablation lesions in a pig liver model. J Surg Res 1999; 87: 114-21. 10.1006/jsre.1999.570910527712Search in Google Scholar

22 Sersa G, Jarm T, Kotnik T, Coer A, Podkrajsek M, Sentjurc M, et al. Vascular disrupting action of electroporation and electrochemotherapy with bleomycin in murine sarcoma. Br J Cancer 2008; 98: 388-98. 10.1038/sj.bjc.6604168236146418182988Search in Google Scholar

23 Lesage D, Angelini ED, Bloch I, Funka-Lea G. A review of 3D vessel lumen segmentation techniques: models, features and extraction schemes. Med Image Anal 2009; 13: 819-45. 10.1016/j.media.2009.07.011Search in Google Scholar

24 Glombitza G, Lamade W, Demiris AM, Gopfert M, Mayer A, Bahner ML, et al. Virtual planning of liver resections: image processing, visualization and volumetric evaluation. Int J Med Inform 1999; 53: 225-37. 10.1016/S1386-5056(98)00162-2Search in Google Scholar

25 Zahlten C, Jürgens H, Evertsz CJG, Leppek R, Peitgen HO, Klose KJ. Portal vein reconstruction based on topology. Eur J Radiol 1995; 19: 96-100. 10.1016/0720-048X(94)00578-ZSearch in Google Scholar

26 Selle D, Preim B, Schenk A, Peitgen HO. Analysis of vasculature for liver surgical planning. IEEE Trans Med Imaging 2002; 21: 1344-57. 10.1109/TMI.2002.801166Search in Google Scholar

27 Sato Y, Nakajima S, Shiraga N, Atsumi H, Yoshida S, Koller T, et al. Threedimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. Med Image Anal 1998; 2: 143-68. 10.1016/S1361-8415(98)80009-1Search in Google Scholar

28 Frangi AF, Niessen WJ, Vincken KL, Viergever MA. Multiscale vessel enhancement filtering. In: Wells WM, Colchester A, Delp S, editors. Medical Image Computing and Computer-Assisted Intervention - MICCAI ’98 (1998). Berlin, Heidelberg: Springer; 1998. p. 130-7. Search in Google Scholar

29 Krissian K, Malandain G, Ayache N, Vaillant R, Trousset Y. Model-based detection of tubular structures in 3D images. Comput Vis Image Underst 2000; 80: 130-71. 10.1006/cviu.2000.0866Search in Google Scholar

30 Conversano F, Franchini R, Demitri C, Massoptier L, Montagna F, Maffezzoli A, et al. Hepatic vessel segmentation for 3D planning of liver surgery experimental evaluation of a new fully automatic algorithm. Acad Radiol 2011; 18: 461-70. 10.1016/j.acra.2010.11.01521216631Search in Google Scholar

31 Bauer C, Pock T, Sorantin E, Bischof H, Beichel R. Segmentation of interwoven 3d tubular tree structures utilizing shape priors and graph cuts. Med Image Anal 2010; 14: 172-84. 10.1016/j.media.2009.11.00320060769Search in Google Scholar

32 Shang Q, Clements L, Galloway RL, Chapman WC, Dawant BM. Adaptive directional region growing segmentation of the hepatic vasculature. In: Reinhardt JM, Pluim JPW, editors. Proceedings of SPIE. Volume 6914. SPIE; 2008. p. 69141F-10. 10.1117/12.769565Search in Google Scholar

33 Beichel R, Pock T, Janko C, Zotter RB, Reitinger B, Bornik A, et al. Liver segment approximation in CT data for surgical resection planning. In: Fitzpatrick JM, Sonka M, editors. Proceedings of SPIE. SPIE; 2004. p. 1435-46. 10.1117/12.535514Search in Google Scholar

34 Wang G, Zhang S, Li F, Gu L. A new segmentation framework based on sparse shape composition in liver surgery planning system. Med Phys 2013; 40: 051913. 10.1118/1.4802215365121523635283Search in Google Scholar

35 Soler L, Delingette H, Malandain G, Montagnat J, Ayache N, Koehl C, et al. Fully automatic anatomical, pathological, and functional segmentation from CT scans for hepatic surgery. Comput aided Surg Off J Int Soc Comput Aided Surg 2001; 6: 131-42. 10.3109/10929080109145999Search in Google Scholar

36 Pamulapati V, Wood BJ, Linguraru MG. Intra-hepatic vessel segmentation and classification in multi-phase CT using optimized graph cuts. In: Yoshida H, Sakas G, Linguraru MG, editors. 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. Volume 7029. IEEE; 2011. p. 1982-5. 10.1109/ISBI.2011.5872799Search in Google Scholar

37 Esneault S, Lafon C, Dillenseger J-L. Liver vessels segmentation using a hybrid geometrical moments/graph cuts method. IEEE Trans Biomed Eng 2010; 57: 276-83. 10.1109/TBME.2009.2032161283140019783500Search in Google Scholar

38 Shang Y, Deklerck R, Nyssen E, Markova A, de Mey J, Yang X, et al. Vascular active contour for vessel tree segmentation. IEEE Trans Biomed Eng 2011; 58: 1023-32. 10.1109/TBME.2010.209759621138795Search in Google Scholar

39 Chi Y, Liu J, Venkatesh SK, Huang S, Zhou J, Tian Q, et al. Segmentation of liver vasculature from contrast enhanced CT images using context-based voting. IEEE Trans Biomed Eng 2011; 58: 2144-53. 10.1109/TBME.2010.209352321095856Search in Google Scholar

40 Bipat S, van Leeuwen MS, Comans EFI, Pijl MEJ, Bossuyt PMM, Zwinderman AH, et al. Colorectal liver metastases: CT, MR imaging, and PET for diagnosis- -meta-analysis. Radiology 2005; 237: 123-31. 10.1148/radiol.237104206016100087Search in Google Scholar

41 Chan VO, Das JP, Gerstenmaier JF, Geoghegan J, Gibney RG, Collins CD, et al. Diagnostic performance of MDCT, PET/CT and gadoxetic acid (Primovist(®))- enhanced MRI in patients with colorectal liver metastases being considered for hepatic resection: initial experience in a single centre. Ir J Med Sci 2012; 181: 499-509. 10.1007/s11845-012-0805-x22426901Search in Google Scholar

42 Floriani I, Torri V, Rulli E, Garavaglia D, Compagnoni A, Salvolini L, et al. Performance of imaging modalities in diagnosis of liver metastases from colorectal cancer: a systematic review and meta-analysis. J Magn Reson Imaging 2010; 31: 19-31. 10.1002/jmri.2201020027569Search in Google Scholar

43 Fowler KJ, Linehan DC, Menias CO. Colorectal liver metastases: state of the art imaging. Ann Surg Oncol 2013; 20: 1185-93. 10.1245/s10434-012-2730-723115006Search in Google Scholar

44 Mainenti PP, Mancini M, Mainolfi C, Camera L, Maurea S, Manchia A, et al. Detection of colo-rectal liver metastases: prospective comparison of contrast enhanced US, multidetector CT, PET/CT, and 1.5 Tesla MR with extracellular and reticulo-endothelial cell specific contrast agents. Abdom Imaging 2010; 35: 511-21. Search in Google Scholar

45 Muhi A, Ichikawa T, Motosugi U, Sou H, Nakajima H, Sano K, et al. Diagnosis of colorectal hepatic metastases: comparison of contrast-enhanced CT, contrast-enhanced US, superparamagnetic iron oxide-enhanced MRI, and gadoxetic acid-enhanced MRI. J Magn Reson Imaging 2011; 34: 326-35. 10.1002/jmri.22613Search in Google Scholar

46 Kranjc M, Bajd F, Serša I, Miklavčič D. Magnetic resonance electrical impedance tomography for monitoring electric field distribution during tissue electroporation. IEEE Trans Med Imaging 2011; 30: 1771-8. 10.1109/TMI.2011.2147328Search in Google Scholar

47 Kranjc M, Bajd F, Sersa I, Woo EJ, Miklavcic D. Ex vivo and in silico feasibility study of monitoring electric field distribution in tissue during electroporation- based treatments. PLoS One 2012; 7: e45737. 10.1371/journal.pone.0045737Search in Google Scholar

48 Pavliha D, Kos B, Marčan M, Zupanič A, Serša G, Miklavčič D. Planning of electroporation-based treatments using Web-based treatment planning software. J Membr Biol 2013; 246: 833-42. 10.1007/s00232-013-9567-2Search in Google Scholar

49 Vovk U, Pernus F, Likar B. A review of methods for correction of intensity inhomogeneity in MRI. IEEE Trans Med Imaging 2007; 26: 405-21. 10.1109/TMI.2006.891486Search in Google Scholar

50 Zheng Y, Grossman M, Awate SP, Gee JC. Automatic correction of intensity nonuniformity from sparseness of gradient distribution in medical images. Med Image Comput Comput Assist Interv 2009; 12: 852-9. 10.1007/978-3-642-04271-3_103Search in Google Scholar

51 Sankur B. Survey over image thresholding techniques and quantitative performance evaluation. J Electron Imaging 2004; 13: 146. 10.1117/1.1631315Search in Google Scholar

52 Otsu N. A Threshold Selection Method from Gray-Level Histograms. IEEE Trans Syst Man Cybern 1979; 9: 62-6. 10.1109/TSMC.1979.4310076Search in Google Scholar

53 Kapur JN, Sahoo PK, Wong AKC. A new method for gray-level picture thresholding using the entropy of the histogram. Comput Vision, Graph Image Process 1985; 29: 273-85. 10.1016/0734-189X(85)90125-2Search in Google Scholar

54 Yaroslavsky LP. Efficient algorithm for discrete sinc interpolation. Appl Opt 1997; 36: 460-3. 10.1364/AO.36.00046018250694Search in Google Scholar

55 Van Dongen E, van Ginneken B. Automatic segmentation of pulmonary vasculature in thoracic CT scans with local thresholding and airway wall removal. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE; 2010. p. 668-71. 10.1109/ISBI.2010.5490088Search in Google Scholar

56 Augusto L, Braga F, Silveira C, Paula V, Fazan S. Arterial diameter of the celiac trunk and its branches. Anatomical study 1 Diâmetro arterial do tronco celíaco e seus ramos. Estudo Anatômico 2009; 24: 43-7 . 10.1590/S0102-86502009000100009Search in Google Scholar

57 Olabarriaga S., Breeuwer M, Niessen W. Evaluation of Hessian-based filters to enhance the axis of coronary arteries in CT images. Int Congr Ser 2003; 1256: 1191-6. 10.1016/S0531-5131(03)00307-8Search in Google Scholar

58 Merkx M a G, Bescós JO, Geerts L, Bosboom EMH, van de Vosse FN, Breeuwer M. Accuracy and precision of vessel area assessment: manual versus automatic lumen delineation based on full-width at half-maximum. J Magn Reson Imaging 2012; 36: 1186-93. 10.1002/jmri.23752Search in Google Scholar

59 Jiang J, Haacke EM, Dong M. Dependence of vessel area accuracy and precision as a function of MR imaging parameters and boundary detection algorithm. J Magn Reson Imaging 2007; 25: 1226-34. 10.1002/jmri.20918Search in Google Scholar

60 Virtanen JM, Komu ME, Parkkola RK. Quantitative liver iron measurement by magnetic resonance imaging: in vitro and in vivo assessment of the liver to muscle signal intensity and the R2* methods. Magn Reson Imaging 2008; 26: 1175-82. 10.1016/j.mri.2008.01.028Search in Google Scholar

61 Deng X, Du G. Editorial: 3D segmentation in the clinic: A grand challenge II-liver tumor segmentation. In: International Conference on Medical Image Computing and Computer Assisted Intervention. 2008. p. 1-12. Search in Google Scholar

62 Van Erkel a R, Pattynama PM. Receiver operating characteristic (ROC) analysis: basic principles and applications in radiology. Eur J Radiol 1998; 27: 88-94. 10.1016/S0720-048X(97)00157-5Search in Google Scholar

63 Obuchowski NA. Receiver operating characteristic curves and their use in radiology. Radiology 2003; 229: 3-8. 10.1148/radiol.229101089814519861Search in Google Scholar

64 Wagner RF, Metz CE, Campbell G. Assessment of medical imaging systems and computer aids: a tutorial review. Acad Radiol 2007; 14: 723-48. 10.1016/j.acra.2007.03.00117502262Search in Google Scholar

65 Hou Z, Hu Q, Nowinski WL. On minimum variance thresholding. Pattern Recognit Lett 2006; 27: 1732-43. 10.1016/j.patrec.2006.04.012Search in Google Scholar

66 Medina-Carnicer R, Madrid-Cuevas FJ. Unimodal thresholding for edge detection. Pattern Recognit 2008; 41: 2337-46. 10.1016/j.patcog.2007.12.007Search in Google Scholar

67 Xu X, Xu S, Jin L, Song E. Characteristic analysis of Otsu threshold and its applications. Pattern Recognit Lett 2011; 32: 956-61. 10.1016/j.patrec.2011.01.021Search in Google Scholar

68 Heimann T, Van Ginneken B, Styner MA, Arzhaeva Y, Aurich V, Bauer C, et al. Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans Med Imaging 2009; 28: 1251-65. 10.1109/TMI.2009.201385119211338Search in Google Scholar

69 Christina Lee W-C, Tublin ME, Chapman BE. Registration of MR and CT images of the liver: comparison of voxel similarity and surface based registration algorithms. Comput Methods Programs Biomed 2005; 78: 101-14. 10.1016/j.cmpb.2004.12.00615848266Search in Google Scholar

70 Elhawary H, Oguro S, Tuncali K, Morrison PR, Tatli S, Shyn PB, et al. Multimodality non-rigid image registration for planning, targeting and monitoring during CT-guided percutaneous liver tumor cryoablation. Acad Radiol 2010; 17: 1334-44. 10.1016/j.acra.2010.06.004295266520817574Search in Google Scholar

eISSN:
1581-3207
Lingua:
Inglese
Frequenza di pubblicazione:
4 volte all'anno
Argomenti della rivista:
Medicine, Clinical Medicine, Internal Medicine, Haematology, Oncology, Radiology