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Survey on Liver Tumour Resection Planning System: Steps, Techniques, and Parameters

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Abstract

Preoperative planning for liver surgical treatments is an essential planning tool that aids in reducing the risks of surgical resection. Based on the computed tomography (CT) images, the resection can be planned before the actual tumour resection surgery. The computer-aided system provides an overview of the spatial relationships of the liver organ and its internal structures, tumours, and vasculature. It also allows for an accurate calculation of the remaining liver volume after resection. The aim of this paper was to review the main stages of the computer-aided system that helps to evaluate the risk of resection during liver cancer surgical treatments. The computer-aided system assists with surgical planning by enabling physicians to get volumetric measurements and visualise the liver, tumours, and surrounding vasculature. In this paper, it is concluded that for accurate planning of tumour resections, the liver organ and its internal structures should be segmented to understand the clear spatial relationship between them, thus allowing for a safer resection. This paper presents the main proposed segmentation techniques for each stage in the computer-aided system, namely the liver organ, tumours, and vessels. From the reviewed methods, it has been found that instead of relying on a single specific technique, a combination of a group of techniques would give more accurate segmentation results. The extracted masks from the segmentation algorithms are fused together to give the surgeons the 3D visualisation tool to study the spatial relationships of the liver and to calculate the required resection planning parameters.

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References

  1. Couinaud C: Liver anatomy: portal and suprahepatic or biliary segmentation. Dig Surg 16:459–467, 1999

    CAS  PubMed  Google Scholar 

  2. Foruzan AH, Chen Y-W, Zoroofi RA, Kaibori M: Analysis of CT Images of Liver for Surgical Planning. Am J Biomed Eng 2:23–28, 2012

    Google Scholar 

  3. Zahel T et al.: Rapid assessment of liver volumetry by a novel automated segmentation algorithm. J Comput Assist Tomogr 37:577–582, 2013

    PubMed  Google Scholar 

  4. El Khodary M, Milot L, Reinhold C: Imaging of Hepatic Metastases. In Liver Metastasis: Biology and Clinical Management 307–351, 2011

  5. Neumann UP, Seehofer D, Neuhaus P: The surgical treatment of hepatic metastases in colorectal carcinoma. Dtsch Arztebl Int 107:335–342, 2010

    PubMed  PubMed Central  Google Scholar 

  6. Hackl C, Schlitt HJ, Renner P, Lang SA: Liver surgery in cirrhosis and portal hypertension. World J Gastroenterol 22:2725–2735, 2016

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Grundmann R et al.: Diagnostik und Therapie von Lebermetastasen kolorektaler Karzinome - Workflow. Zentralbl Chir 133:267–284, 2008

    CAS  PubMed  Google Scholar 

  8. Scherer MA, Geller DA: New Preoperative Images, Surgical Planning, and Navigation. In: Imaging and Visualization in The Modern Operating Room. New York: Springer, 2015, pp. 205–214

    Google Scholar 

  9. Coulon P, De Brouwer F, Steinberg A: Clinical uses for CT Liver Analysis application, 2013

  10. Lang H et al.: Impact of Virtual Tumor Resection and Computer-Assisted Risk Analysis on Operation Planning and Intraoperative Strategy in Major Hepatic Resection. Arch Surg 140:629, 2005

    PubMed  Google Scholar 

  11. Fong JS, Ibrahim H: Development of a virtual reality system for Hepatocellular Carcinoma pre-surgical planning. in 2010 2nd International Conference on Software Technology and Engineering IEEE, 1, 2010, pp 1–41

  12. Meinzer H et al.: Computer-based Surgery Planning For Living Liver Donation. Transplantation 78(2):173–174, 2004

    Google Scholar 

  13. Selle D, Preim B, Schenk A, Peitgen HO: Analysis of vasculature for liver surgical planning. IEEE Trans Med Imaging 21:1344–1357, 2002

    PubMed  Google Scholar 

  14. Shevchenko N, Seidl B, Schwaiger J, Markert M, Lueth TC: MiMed liver: A planning system for liver surgery. 2010 Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBC’10 1882–1885, 2010

  15. American Cancer Society. Facts & Figures. Atlanta, Ga, 2019

  16. El Khodary M, Milot L, Reinhold C: Imaging of Hepatic Metastases. Dordrecht: Springer, 2011, pp. 307–351

    Google Scholar 

  17. Soyer P et al.: Detection of hypovascular hepatic metastases at triple-phase helical CT: sensitivity of phases and comparison with surgical and histopathologic findings. Radiology 231:413–420, 2004

    PubMed  Google Scholar 

  18. Khalil HI, Patterson SA, Panicek DM: Hepatic lesions deemed too small to characterize at CT: prevalence and importance in women with breast cancer. Radiology 235:872–878, 2005

    PubMed  Google Scholar 

  19. Sahani D, Kalva S, Tanabe K, Hayat S: Intraoperative US in patients undergoing surgery for liver neoplasms: comparison with MR imaging 1. Radiology 232(3):810–814, 2004

    PubMed  Google Scholar 

  20. Zaheer S: Fast segmentation of vessels in MR liver images using patient specific models. PhD diss., 2013

  21. Assistant Radiology. Liver - Incidentalomas. http://www.radiologyassistant.nl/2017. Accessed 12 May 2007

  22. Pamulapati V, Venkatesan A, Wood BJ, Linguraru MG: Liver segmental anatomy and analysis from vessel and tumor segmentation via optimized graph cuts. In: Abdominal Imaging. Computational and Clinical Applications. Berlin: Springer, 2012, pp. 189–197

    Google Scholar 

  23. Linguraru MG, Sandberg JK, Li Z, Shah F, Summers RM: Automated segmentation and quantification of liver and spleen from CT images using normalized probabilistic atlases and enhancement estimation. Med Phys 37:771–783, 2010

    PubMed  PubMed Central  Google Scholar 

  24. Boas FE, Fleischmann D: CT artifacts: causes and reduction techniques. Imaging Med 4:229–240, 2012

    Google Scholar 

  25. Campadelli P, Casiraghi E: Liver segmentation from CT scans: A survey. In: Applications of Fuzzy Sets Theory. Berlin: Springer, 2007, pp. 520–528

    Google Scholar 

  26. Hermoye L et al.: Liver Segmentation in Living Liver Transplant Donors: Comparison of Semiautomatic and Manual Methods 1. Radiology 234:171–178, 2005

    PubMed  Google Scholar 

  27. Priyadarsini S, Selvathi D: Survey on segmentation of liver from CT images. Proc. 2012 IEEE Int. Conf. Adv. Commun. Control Comput. Technol. ICACCCT, 2012, pp. 234–238

  28. Campadelli P, Casiraghi E, Esposito A: Liver segmentation from computed tomography scans: A survey and a new algorithm. Artif Intell Med 45:185–196, 2009

    PubMed  Google Scholar 

  29. Luo S, Li X, Li J: Review on the Methods of Automatic Liver Segmentation from Abdominal Images. J Comput Commun 2:1–7, 2014

    CAS  Google Scholar 

  30. Mharib AM, Ramli AR, Mashohor S, Mahmood RB: Survey on liver CT image segmentation methods. Artif Intell Rev 37:83–95, 2012

    Google Scholar 

  31. Heimann T et al.: Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans Med Imaging 28:1251–1265, 2009

    PubMed  Google Scholar 

  32. Mohammed FA, Viriri S: Liver segmentation: A survey of the state-of-the-art. in 2017 Sudan Conference on Computer Science and Information Technology SCCSIT 1–6 IEEE, 2017

  33. Niessen WJ, Bouma CJ, Vincken KL, Viergever MA: Error Metrics for Quantitative Evaluation of Medical Image Segmentation. Dordrecht: Springer, 2000, pp. 275–284

    Google Scholar 

  34. Bouix S et al.: On evaluating brain tissue classifiers without a ground truth. Neuroimage 36:1207–1224, 2007

    PubMed  PubMed Central  Google Scholar 

  35. Shimizu A, Nawano S: Preliminary report of competition for liver region extraction algorithms from three-dimensional CT images. Int Congr Ser Elsevier, vol 1268, 2004

  36. Rusko L, Bekes G: Fully automatic liver segmentation for contrast-enhanced CT images. Int Conf Med Image Comput Comput Interv Segmentation Clin a Gd challenge, 2007, pp 143–150

  37. Kumar S, Moni R, Rajeesh J: Automatic segmentation of liver and tumor for CAD of liver. J Adv Inf 2(1):63–70, 2011

    Google Scholar 

  38. Zhao B et al.: Shape-Constraint Region Growing for Delineation of Hepatic Metastases on Contrast-Enhanced Computed Tomograph Scans. Investig Radiol 41:753–762, 2006

    Google Scholar 

  39. Foruzan AH et al: Multi-mode narrow-band thresholding with application in liver segmentation from low-contrast CT images. IIH-MSP 2009–2009 5th Int. Conf. Intell. Inf. Hiding Multimed. Signal Process, 2009, pp 1293–1296

  40. Kumar SS, Moni RS, Rajeesh J: Automatic liver and lesion segmentation: A primary step in diagnosis of liver diseases. SIViP 7:163–172, 2013

    Google Scholar 

  41. Wang Q, Song X, Jiang Z: An Improved Image Segmentation Method Using Three-dimensional Region Growing Algorithm. In: Proceedings of the 2013 International Conference on Information Science and Computer Applications ISCA 2013. Atlantis Press, 2013

  42. Chen Y, Wang Z, Zhao W, Yang X: Liver Segmentation from CT Images Based on Region Growing Method. 3rd Int. Conf. Bioinforma. Biomed. Eng, 2009, pp 1–4

  43. Glocker, B. et al. Primal/Dual Linear Programming and Statistical Atlases for Cartilage Segmentation. in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007 Springer Berlin Heidelberg, 536–543, 2007.

  44. Slagmolen P, Elen A, Seghers D, Loeckx D: Atlas based liver segmentation using nonrigid registration with a B-spline transformation model. In Proceedings of MICCAI workshop on 3D segmentation in the clinic: a grand challenge, 2007, pp 197–206

  45. Cootes TTF, Hill A, Taylor CJC, Haslam J: Use of active shape models for locating structures in medical images. Image Vis Comput 12:355–365, 1994

    Google Scholar 

  46. Heimann T, Meinzer H-P: Statistical shape models for 3D medical image segmentation: A review. Med Image Anal 13:543–563, 2009

    PubMed  Google Scholar 

  47. Gao XGX, Su YSY, Li XLX, Tao DTD: A Review of Active Appearance Models. IEEE Trans Syst Man Cybern Part C Appl Rev 40:145–158, 2010

    Google Scholar 

  48. Wu W, Wu S, Zhang R, Zhou Z: Fast Graph Cuts Based Liver and Tumor Segmentation on Olumetric CT Images. DEStech Transactions on Engineering and Technology Research, 2016, pp 3–7

  49. Liao M et al.: Automatic liver segmentation from abdominal CT volumes using graph cuts and border marching. Comput Methods Prog Biomed 143:1–12, 2017

    Google Scholar 

  50. Kainmüller D, Lange T, Lamecker H: Shape constrained automatic segmentation of the liver based on a heuristic intensity model. MICCAI Work. 3D Segmentation Clin. A Gd. Chall, 2007, pp 109–16

  51. Erdt M, et al: Fast automatic liver segmentation combining learned shape priors with observed shape deviation. Proc - IEEE Symp Comput Med Syst, 2010, pp 249–254

  52. Li X et al.: Automatic Liver Segmentation Using Statistical Prior Models and Free-form Deformation. Cham: Springer, 2014, pp. 181–188

    Google Scholar 

  53. Boykov YY, Jolly M-P: Interactive Graph Cuts for Optimal Boundary & Region Segmentation of Objects in N-D Images. Proceedings Eighth IEEE International Conference on Computer Vision, ICCV, 2001, pp 105–11

  54. Platero C, Tobar MC: A multiatlas segmentation using graph cuts with applications to liver segmentation in CT scans. Comput Math Methods Med:182909, 2014

  55. Li G et al.: Automatic Liver Segmentation Based on Shape Constraints and Deformable Graph Cut in CT Images. IEEE Trans Image Process 24:5315–5329, 2015

    PubMed  Google Scholar 

  56. Kass M, Witkin A, Terzopoulos D: Snakes: Active contour models. Int J Comput Vis 1:321–331, 1988

    Google Scholar 

  57. Caselles V, Catt F, Coll T, Dibos F: A geometric model for active contours in image processing. Numer Math 66:1–31, 1993

    Google Scholar 

  58. McInerney T, Terzopoulos D: Deformable models in medical image analysis: a survey. Med Image Anal 2:91–108, 1996

    Google Scholar 

  59. Foruzan AH et al.: Segmentation of liver in low-contrast images using K-means clustering and geodesic active contour algorithms. IEICE Trans Inf Syst E96–D:798–807, 2013

    Google Scholar 

  60. Chan T, Chan T, Vese L, Vese L: Active contour without edges. IEEE Trans Image Process 10:266–277, 2001

    CAS  PubMed  Google Scholar 

  61. Läthén G: Segmentation Methods for Medical Image Analysis : Blood vessels, multi-scale filtering and level set methods, 2010

  62. Goryawala M, Gulec S, Bhatt R, McGoron AJ, Adjouadi M: A Low-Interaction Automatic 3D Liver Segmentation Method Using Computed Tomography for Selective Internal Radiation Therapy. Biomed Res Int 2014:12, 2014

    Google Scholar 

  63. Altarawneh NM, Luo S, Regan B, Sun C: A Modified Distance Regularized Level Set Model for Liver Segmentation from CT Images. An Int J 6:1–1, 2015

    Google Scholar 

  64. Li BN, Chui CK, Chang S, Ong SH: A new unified level set method for semi-automatic liver tumor segmentation on contrast-enhanced CT images. Expert Syst Appl 39:9661–9668, 2012

    Google Scholar 

  65. Linguraru MG et al.: Tumor burden analysis on computed tomography by automated liver and tumor segmentation. IEEE Trans Med Imaging 31:1965–1976, 2012

    PubMed  PubMed Central  Google Scholar 

  66. Anter AM, Azar AT, Hassanien AE, El-Bendary N, Elsoud MA: Automatic computer aided segmentation for liver and hepatic lesions using hybrid segmentations techniques. 2013 Fed Conf Comput Sci Inf Syst FedCSIS, 2013, pp 193–198

  67. Qi Y et al.: Semi-automatic segmentation of liver tumors from CT scans using Bayesian rule-based 3D region growing. MICCAI Work. 41:1–10, 2008

    Google Scholar 

  68. Oliveira DA, Feitosa RQ, Correia MM: Segmentation of liver, its vessels and lesions from CT images for surgical planning. Biomed Eng Online 10:30, 2011

    PubMed  PubMed Central  Google Scholar 

  69. Zhou JY et al.: Liver tumour segmentation using contrast-enhanced multi-detector CT data: Performance benchmarking of three semiautomated methods. Eur Radiol 20:1738–1748, 2010

    PubMed  Google Scholar 

  70. Moltz JH, Bornemann L, Dicken V, Peitgen H-O: Segmentation of liver metastases in CT scans by adaptive thresholding and morphological processing. MICCAI Work 41:195, 2008

    Google Scholar 

  71. Häme Y, Pollari M: Semi-automatic liver tumor segmentation with hidden Markov measure field model and non-parametric distribution estimation. Med Image Anal 16:140–149, 2012

    PubMed  Google Scholar 

  72. Freiman M, Cooper O, Lischinski D, Joskowicz L: Liver tumors segmentation from CTA images using voxels classification and affinity constraint propagation. Int J Comput Assist Radiol Surg 6:247–255, 2011

    PubMed  Google Scholar 

  73. Huang W, et al: Liver Tumor Detection and Segmentation using Kernel-based Extreme Learning Machine. In: Engineering in medicine and biology society (EMBC), 2013 35th annual international conference of the IEEE, 138632, 2013, pp 3662–3665

  74. Wu W, Wu S, Zhou Z, Zhang R, Zhang Y: 3D Liver Tumor Segmentation in CT Images Using Improved Fuzzy C -Means and Graph Cuts. Biomed Res Int 2017:1–11, 2017

    Google Scholar 

  75. Vorontsov E, Abi-Jaoudeh N, Kadoury S: Metastatic Liver Tumor Segmentation Using Texture-Based Omni-Directional Deformable Surface Models. Cham: Springer, 2014, pp. 74–83

    Google Scholar 

  76. Otsu N: A threshold selection method from gray-level histograms. Automatica 11:23–27, 1975

    Google Scholar 

  77. Chaturvedi A, Green P, Caroll J: K-modes clustering. J Classif 18(1):35–55, 2001

    Google Scholar 

  78. Huang Z, Chau K: A new image thresholding method based on Gaussian mixture model q. Appl Math Comput 205:899–907, 2008

    Google Scholar 

  79. Rajagopal R, Subbaiah P: A survey on liver tumor detection and segmentation methods. 10(6): 2681–2685, 2015 .

  80. Rajeesh SSKRSSMJ: Automatic liver and lesion segmentation : a primary step in diagnosis of liver diseases. SIViP 7(1):163–172, 2013

    Google Scholar 

  81. Pamulapati V, Wood BJ, Linguraru MG: Intra-hepatic vessel segmentation and classification in multi-phase CT using optimized graph cuts. in Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium, 2011, pp 1982–1985

  82. Chi Y et al.: Segmentation of liver vasculature from contrast enhanced CT images using context-based voting. IEEE Trans Biomed Eng 58:2144–2153, 2011

    Google Scholar 

  83. Conversano F et al.: Hepatic Vessel Segmentation for 3D Planning of Liver Surgery. Experimental Evaluation of a New Fully Automatic Algorithm. Acad Radiol 18:461–470, 2011

    PubMed  Google Scholar 

  84. Kim D: Hepatic vessel segmentation on contrast enhanced CT image sequence for liver transplantation planning. J Biomed Sci Eng 06:498–503, 2013

    Google Scholar 

  85. Jin J, Yang L, Zhang X, Ding M: Vascular tree segmentation in medical images using Hessian-based multiscale filtering and level set method. Comput Math Methods Med 2013:1–10, 2013

    Google Scholar 

  86. Yureidini A, Kerrien E, Loria ING, Nord-europe IL: Robust RANSAC-based blood vessel segmentation. Proc SPIE 8314:8314M, 2012

    Google Scholar 

  87. Beichel R et al.: Liver segment approximation in CT data for surgical resection planning. Proc SPIE 5370:1435–1446, 2004

    Google Scholar 

  88. Marcan M et al.: Segmentation of hepatic vessels from MRI images for planning of electroporation-based treatments in the liver. Radiol Oncol 48:267–268, 2014

    PubMed  PubMed Central  Google Scholar 

  89. Esneault S, Lafon C, Dillenseger J-L: Liver Vessels Segmentation Using a Hybrid Geometrical Moments/Graph Cuts Method. IEEE Trans Biomed Eng 57:276–283, 2010

    PubMed  Google Scholar 

  90. 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 13:819–845, 2009

    PubMed  Google Scholar 

  91. Rodrigues FM, Silva JS, Rodrigues TM: An algorithm for the surgical planning of hepatic resections. 2012 IEEE 2nd Port. Meet Bioeng ENBENG, 2012, pp 1–6

  92. Mohan V, Sundaramoorthi G, Stillman A, Tannenbaum A: Vessel Segmentation with Automatic Centerline Extraction Using Tubular Tree Segmentation. 8, 2009

  93. Shen Y, Wang B, Ju Y, Xie J: Interaction techniques for the exploration of hepatic vessel structure. Eng Med 2902–2905,2006

  94. Soler L et al.: Fully automatic anatomical, pathological, and functional segmentation from CT scans for hepatic surgery. Comput Aided Surg 6:131–142, 2001

    CAS  PubMed  Google Scholar 

  95. Smistad E, Elster AC, Lindseth F: GPU accelerated segmentation and centerline extraction of tubular structures from medical images. Int J Comput Assist Radiol Surg 9:561–575, 2014

    PubMed  Google Scholar 

  96. Kirbas C, Quek F: A Review of Vessel Extraction Techniques and Algorithms. ACM Comput Surv 36(2):81–121, 2003

    Google Scholar 

  97. Tian Y et al.: A vessel active contour model for vascular segmentation. Biomed Res Int 2014:1–15, 2014

    Google Scholar 

  98. Hong Q et al.: 3D vasculature segmentation using localized hybrid level-set method. Biomed Eng Online 13:169, 2014

    PubMed  PubMed Central  Google Scholar 

  99. Frangi AF, Niessen WJ, Vincken KL, Viergever MA: Multiscale vessel enhancement filtering. Medial Image Comput. Comput. Invervention - MICCAI’98. Lect Notes Comput Sci 1496:130–137, 1998

    Google Scholar 

  100. Manniesing R, Viergever MA, Niessen WJ: Vessel enhancing diffusion. A scale space representation of vessel structures. Med Image Anal 10:815–825, 2006

    PubMed  Google Scholar 

  101. Sato Y et al.: Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. Med Image Anal 2:143–168, 1998

    CAS  PubMed  Google Scholar 

  102. Erdt M, Raspe M, Suehling M: Automatic Hepatic Vessel Segmentation Using Graphics Hardware. In: Medical Imaging and Augmented Reality, 2008, pp 403–412

  103. Luu HM, Klink C, Moelker A, Niessen W, van Walsum T: Quantitative evaluation of noise reduction and vesselness filters for liver vessel segmentation on abdominal CTA images. Phys Med Biol 60:3905–3926, 2015

    PubMed  Google Scholar 

  104. Mendrik AM, Vonken EJ, Rutten A, Viergever MA, Van Ginneken B: Noise reduction in computed tomography scans using 3-D anisotropic hybrid diffusion with continuous switch. IEEE Trans Med Imaging 28:1585–1594, 2009

    PubMed  Google Scholar 

  105. Fasel JHD, Majno PE, Peitgen H-O: Liver segments: an anatomical rationale for explaining inconsistencies with Couinaud’s eight-segment concept. Surg Radiol Anat 32:761–765, 2010

    PubMed  Google Scholar 

  106. Castaing D, Adam R, Azoulay D: Chirurgie du foie et de l’hypertension portale. Masson, 2006

  107. Drechsler K, Erdt M, Laura CO, Wesarg S: Multiphase risk assessment of atypical liver resections. in Computer-Based Medical Systems CBMS , 2012 25th International Symposium on 1–4 IEEE, 2012

  108. Yoon JH, et al: Feasibility of three-dimensional virtual surgical planning in living liver donors. Abdom Imaging, 1–11,2014

  109. Reitinger B, Bornik A, Beichel R, Schmalstieg D: Liver Surgery Planning Using Virtual Reality. IEEE Comput Graph Appl 26:36–47, 2006

    PubMed  Google Scholar 

  110. Debarba HG, Zanchet DJ, Fracaro D, MacIel A, Kalil AN: Efficient liver surgery planning in 3D based on functional segment classification and volumetric information. 2010 Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBC’10, 2010, pp 4797–4800

  111. Zhan Y et al.: Liver vessel segmentation based on extreme learning machine. Phys Medica 32:709–716, 2016

    Google Scholar 

  112. Kaftan JN, Tek H, Aach T: A two-stage approach for fully automatic segmentation of venous vascular structures in liver CT images. Proc SPIE 725911:725911–725912, 2009

    Google Scholar 

  113. Blum HE: Hepatocellular carcinoma: therapy and prevention. World J Gastroenterol 11:7391–7400, 2005

    CAS  PubMed  PubMed Central  Google Scholar 

  114. Clavien P-A, Breitenstein S, Belghiti J: Malignant liver tumors : current and emerging therapies. Hoboken: Wiley-Blackwell Pub, 2010

    Google Scholar 

  115. Forner A, Llovet JM, Bruix J: Hepatocellular carcinoma. Lancet 379:1245–1255, 2012

    PubMed  Google Scholar 

  116. Kim KW et al.: Right Lobe Estimated Blood-free Weight for Living Donor Liver Transplantation: Accuracy of Automated Blood-free CT Volumetry—Preliminary Results. Radiology 256:433–440, 2010

    PubMed  Google Scholar 

  117. Mokry T et al.: Accuracy of Estimation of Graft Size for Living-Related Liver Transplantation: First Results of a Semi-Automated Interactive Software for CT-Volumetry. PLoS One 9:e110201, 2014

    PubMed  PubMed Central  Google Scholar 

  118. Yang X et al.: Dr Liver: A preoperative planning system of liver graft volumetry for living donor liver transplantation. Comput Methods Prog Biomed 158:11–19, 2018

    Google Scholar 

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This research is supported by the Malaysian Ministry of Higher Education and Universiti Kebangsaan Malaysia (grant number GUP-2014-066).

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Alirr, O.I., Rahni, A.A.A. Survey on Liver Tumour Resection Planning System: Steps, Techniques, and Parameters. J Digit Imaging 33, 304–323 (2020). https://doi.org/10.1007/s10278-019-00262-8

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