Segmentation of hepatic vessels from MRI images for planning of electroporation-based treatments in the liver

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Abstract

Introduction. Electroporation-based treatments rely on increasing the permeability of the cell membrane by high voltage electric pulses delivered to tissue via electrodes. To ensure that the whole tumor is covered by the sufficiently high electric field, accurate numerical models are built based on individual patient geometry. For the purpose of reconstruction of hepatic vessels from MRI images we searched for an optimal segmentation method that would meet the following initial criteria: identify major hepatic vessels, be robust and work with minimal user input.

Materials and methods. We tested the approaches based on vessel enhancement filtering, thresholding, and their combination in local thresholding. The methods were evaluated on a phantom and clinical data. Results.

Results show that thresholding based on variance minimization provides less error than the one based on entropy maximization. Best results were achieved by performing local thresholding of the original de-biased image in the regions of interest which were determined through previous vessel-enhancement filtering. In evaluation on clinical cases the proposed method scored in average sensitivity of 93.68%, average symmetric surface distance of 0.89 mm and Hausdorff distance of 4.04 mm.

Conclusions. The proposed method to segment hepatic vessels from MRI images based on local thresholding meets all the initial criteria set at the beginning of the study and necessary to be used in treatment planning of electroporation- based treatments: it identifies the major vessels, provides results with consistent accuracy and works completely automatically. Whether the achieved accuracy is acceptable or not for treatment planning models remains to be verified through numerical modeling of effects of the segmentation error on the distribution of the electric field.

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.

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.

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.

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.

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.

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.

7 Davalos R V., Mir LM, Rubinsky B. Tissue Ablation with Irreversible Electroporation. Ann Biomed Eng 2005; 33: 223-31.

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

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 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.

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.

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.

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.

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.

15 Županič A, Čorović S, Miklavčič D. Optimization of electrode position and electric pulse amplitude in electrochemotherapy. Radiol Oncol 2008; 42: 93-101.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

26 Selle D, Preim B, Schenk A, Peitgen HO. Analysis of vasculature for liver surgical planning. IEEE Trans Med Imaging 2002; 21: 1344-57.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

43 Fowler KJ, Linehan DC, Menias CO. Colorectal liver metastases: state of the art imaging. Ann Surg Oncol 2013; 20: 1185-93.

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.

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.

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.

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.

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.

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.

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.

51 Sankur B. Survey over image thresholding techniques and quantitative performance evaluation. J Electron Imaging 2004; 13: 146.

52 Otsu N. A Threshold Selection Method from Gray-Level Histograms. IEEE Trans Syst Man Cybern 1979; 9: 62-6.

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.

54 Yaroslavsky LP. Efficient algorithm for discrete sinc interpolation. Appl Opt 1997; 36: 460-3.

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.

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 .

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.

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.

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.

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.

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.

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.

63 Obuchowski NA. Receiver operating characteristic curves and their use in radiology. Radiology 2003; 229: 3-8.

64 Wagner RF, Metz CE, Campbell G. Assessment of medical imaging systems and computer aids: a tutorial review. Acad Radiol 2007; 14: 723-48.

65 Hou Z, Hu Q, Nowinski WL. On minimum variance thresholding. Pattern Recognit Lett 2006; 27: 1732-43.

66 Medina-Carnicer R, Madrid-Cuevas FJ. Unimodal thresholding for edge detection. Pattern Recognit 2008; 41: 2337-46.

67 Xu X, Xu S, Jin L, Song E. Characteristic analysis of Otsu threshold and its applications. Pattern Recognit Lett 2011; 32: 956-61.

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.

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.

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.

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