JSEA  Vol.7 No.2 , February 2014
Image Processing Tool Promoting Decision-Making in Liver Surgery of Patients with Chronic Kidney Disease
ABSTRACT

Preoperative assessment of the liver volume and function of the remnant liver is a mandatory prerequisite before performing major hepatectomy. The aim of this work is to develop and test a software application for evaluation of the residual function of the liver prior to the intervention of the surgeons. For this purpose, a complete software platform consisting of three basic modules: liver volume segmentation, visualization, and virtual cutting, was developed and tested. Liver volume segmentation is based on a patient examination with non-contrast abdominal Computed Tomography (CT). The basis of the segmentation is a multiple seeded region growing algorithm adapted for use with CT images without contrast-enhancement. Virtual tumor resection is performed interactively by outlining the liver region on the CT images. The software application then processes the results to produce a three-dimensional (3D) image of the resected region. Finally, 3D rendering module provides possibility for easy and fast interpretation of the segmentation results. The visual outputs are accompanied with quantitative measures that further provide estimation of the residual liver function and based on them the surgeons could make a better decision. The developed system was tested and verified with twenty abdominal CT patient sets consisting of different numbers of tomographic images. Volumes, obtained by manual tracing of two surgeon experts, showed a mean relative difference of 4.5%. The application was used in a study that demonstrates the need and the added value of such a tool in practice and in education.


Cite this paper
K. Bliznakova, N. Kolev, Z. Bliznakov, I. Buliev, A. Tonev, E. Encheva and K. Ivanov, "Image Processing Tool Promoting Decision-Making in Liver Surgery of Patients with Chronic Kidney Disease," Journal of Software Engineering and Applications, Vol. 7 No. 2, 2014, pp. 118-127. doi: 10.4236/jsea.2014.72013.
References
[1]   E. Benzoni, R. Molaro, C. Cedolini, A. Favero, A. Cojutti, D. Lorenzin, et al., “Liver Resection for HCC: Analysis of Causes and Risk Factors Linked to Postoperative Complications,” Hepatogastroen terology, Vol. 54, No. 73, 2007, pp. 186-199.

[2]   M. J. Schindl, D. N. Redhead, K. C. Fearon, O. J. Garden and S. J. Wigmore, “The Value of Residual Liver Volume as a Predictor of Hepatic Dysfunction and Infection after Major Liver Resection,” Gut, Vol. 54, No. 2, 2005, pp. 289-296. http://dx.doi.org/10.1136/gut.2004.046524

[3]   A. Guglielmi, A. Ruzzenente, S. Conci, A. Valdegamberi and C. Iacono, “How Much Remnant Is Enough in Liver Resection?” Digestive Surgery, Vol. 29, No. 1, 2012, pp. 6-17. http://dx.doi.org/10.1159/ 000335713

[4]   Y. Nakayama, Q. Li, S. Katsuragawa, R. Ikeda, Y. Hiai, K. Awai, et al., “Automated Hepatic Volumetry for Living Related Liver Transplantation at Multisection CT,” Radiology, Vol. 240, No. 3, 2006, pp. 743-748. http://dx.doi.org/10.1148/radiol.2403050850

[5]   S. Casciaro, R. Franchini, L. Massoptier, E. Casciaro, F. Conversano, A. Malvasi, et al., “Fully Automatic Segmentations of Liver and Hepatic Tumors from 3-D Computed Tomography Abdominal Images: Comparative Evaluation of Two Automatic Methods,” IEEE Sensors Journal, Vol. 12, No. 3, 2012, pp. 464-473. http://dx.doi.org/10.1109/JSEN.2011.2108281

[6]   K. T. Bae, M. L. Giger, C. T. Chen and C. E. Kahn Jr., “Automatic Segmentation of Liver Structure in CT Images,” Medical Physics, Vol. 20, No. 1, 1993, pp. 71-78. http://dx.doi.org/10.1118/1.597064

[7]   E. L. Chen, P. C. Chung, C. L. Chen, H. M. Tsai and C. I. Chang, “An Automatic Diagnostic System for CT Liver Image Classification,” IEEE Transactions on Biomedical Engineering, Vol. 45, No. 6, 1998, pp. 783-794. http://dx.doi.org/10.1109/10.678613

[8]   L. Soler, H. Delingette, G. Malandain, J. Montagnat, N. Ayache, C. Koehl, et al., “Fully Automatic Anatomical, Pathological and Functional Segmentation from CT Scans for Hepatic Surgery,” Computer Aided Surgery, Vol. 6, No. 3, 2001, pp. 131-142. http://dx.doi.org/10.3109/10929080109145999

[9]   L. Massoptier and S. Casciaro, “Fully Automatic Liver Segmentation through Graph-Cut Technique,” IEEE Engineering in Medicine & Biology Society, Vol. 2007, 2007, pp. 5243-5246.
http://dx.doi.org/10.1109/IEMBS.2007.4353524

[10]   L. Massoptier and S. Casciaro, “A New Fully Automatic and Robust Algorithm for Fast Segmentation of Liver Tissue and Tumors from CT Scans,” European Radiology, Vol. 18, No. 8, 2008, pp. 1658-1665. http://dx.doi.org/10.1007/s00330-008-0924-y

[11]   P. Campadelli, E. Casiraghi and A. Esposito, “Liver Segmentation from Computed Tomography Scans: A Survey and a New Algorithm,” Artificial Intelligence in Medicine, Vol. 45, No. 2-3, 2009, pp. 185-196. http://dx.doi.org/10.1016/j.artmed.2008.07.020

[12]   X. Yang, H. C. Yu, Y. Choi, W. Lee, B. Wang, J. Yang, et al., “A Hybrid Semi-Automatic Method for Liver Segmentation Based on Level-Set Methods Using Multiple Seed Points,” Computer Methods and Programs in Biomedicine, Vol. 113, No. 1, 2014, pp. 69-79. http://dx.doi.org/10.1016/j.cmpb.2013. 08.019

[13]   T. Zahel, M. Wildgruber, R. Ardon, T. Schuster, E. J. Rummeny and M. Dobritz, “Rapid Assessment of Liver Volumetry by a Novel Automated Segmentation Algorithm,” Journal of Computer Assisted Tomography, Vol. 37, No. 4, 2013, pp. 577-582. http://dx.doi.org/10.1097/RCT.0b013e31828f0baa

[14]   W. Huang, Z. M. Tan, Z. Lin, G. B. Huang, J. Zhou, C. K. Chui, et al., “A Semi-Automatic Approach to the Segmentation of Liver Parenchyma from 3D CT Images with Extreme Learning Machine,” IEEE Engineering in Medicine & Biology Society, Vol. 2012, 2012, pp. 3752-3755.

[15]   Y. Chen, Z. Wang, J. Hu, W. Zhao and Q. Wu, “The Domain Knowledge Based Graph-Cut Model for Liver CT Segmentation,” Biomedical Signal Processing and Control, Vol. 7, No. 6, 2012, pp. 591-598. http://dx.doi.org/10.1016/j.bspc.2012.04.005

[16]   B. N. Li, C. K. Chui, S. Chang and S. H. Ong, “A New Unified Level Set Method for Semi-Automatic Liver Tumor Segmentation on Contrast-Enhanced CT Images,” Expert Systems with Applications, Vol. 39, No. 10, 2012, pp. 9661-9668. http://dx.doi.org/10.1016/j.eswa.2012.02.095

[17]   M. Goryawala, M. R. Guillen, M. Cabrerizo, A. Barreto, S. Gulec, T. C. Barot, et al., “A 3-D Liver Segmentation Method with Parallel Computing for Selective Internal Radiation Therapy,” IEEE Transactions on Information Technology in Biomedicine, Vol. 16, No. 1, 2012, pp. 62-69.
http://dx.doi.org/10.1109/TITB.2011.2171191

[18]   Y. Hame and M. Pollari, “Semi-Automatic Liver Tumor Segmentation with Hidden Markov Measure Field Model and Non-Parametric Distribution Estimation,” Medical Image Analysis, Vol. 16, No. 1, 2012, pp. 140-149. http://dx.doi.org/10.1016/j.media.2011.06.006

[19]   F. Liu, B. Zhao, P. K. Kijewski, L. Wang and L. H. Schwartz, “Liver Segmentation for CT Images Using GVF Snake,” Medical Physics, Vol. 32, No. 12, 2005, pp. 3699-3706. http://dx.doi.org/10.1118/1. 2132573

[20]   M. Freiman, O. Eliassaf, Y. Taieb, L. Joskowicz and J. Sosna, “A Bayesian Approach for Liver Analysis: Algorithm and Validation Study,” Medical Image Computing and Computer-Assisted Intervention, Vol. 11, No. 1, 2008, pp. 85-92.

[21]   M. Freiman, O. Eliassaf, Y. Taieb, L. Joskowicz, Y. Azraq and J. Sosna, “An Iterative Bayesian Approach for Nearly Automatic Liver Segmentation: Algorithm and Validation,” International Journal of Computer Assisted Radiology and Surgery, Vol. 3, No. 5, 2008, pp. 439-446.
http://dx.doi.org/10.1007/s11548-008-0254-1

[22]   J. Y. Zhou, D. W. K. Wong, F. Ding, S. K. Venkatesh, Q. Tian, Y. Y. Qi, et al., “Liver Tumour Segmentation Using Contrast-Enhanced Multi-Detector CT Data: Performance Benchmarking of Three Semiautomated Methods,” European Radiology, Vol. 20, No. 7, 2010, pp. 1738-1748.
http://dx.doi.org/10.1007/s00330-010-1712-z

[23]   T. Heimann, B. van Ginneken, M. A. Styner, Y. Arzhaeva, V. Aurich, C. Bauer, et al., “Comparison and Evaluation of Methods for Liver Segmentation from CT Datasets,” IEEE Transactions on Medical Imaging, Vol. 28, No. 8, 2009, pp. 1251-1265. http://dx.doi.org/10.1109/TMI.2009.2013851

[24]   F. Liu, B. Zhao, P. K. Kijewski, L. Wang and L. H. Schwartz, “Liver Segmentation for CT Images Using GVF Snake,” Medical Physics, Vol. 32, No. 12, 2005, pp. 3699-3706. http://dx.doi.org/10.1118/1. 2132573

[25]   S. J. Lim, Y. Y. Jeong and Y. S. Ho, “Automatic Liver Segmentation for Volume Measurement in CT Images,” Journal of Visual Communication and Image Representation, Vol. 17, No. 4, 2006, pp. 860-875. http://dx.doi.org/10.1016/j.jvcir.2005.07.001

[26]   L. Gao, D. G. Heath, B. S. Kuszyk and E. K. Fishman, “Automatic Liver Segmentation Technique for Three-Dimensional Visualization of CT Data,” Radiology, Vol. 201, No. 2, 1996, pp. 359-364.

[27]   P. Campadelli, E. Casiraghi and S. Pratissoli, “A Segmentation Framework for Abdominal Organs from CT Scans,” Artificial Intelligence in Medicine, Vol. 50, No. 1, 2010, pp. 3-11.
http://dx.doi.org/10.1016/j.artmed.2010.04.010

[28]   S. S. Kumar, R. S. Moni and J. Rajeesh, “Automatic Segmentation of Liver Tumour Using a Possibilistic Alternative Fuzzy C-Means Clustering,” International Journal of Computers and Applications, Vol. 35, No. 1, 2013, pp. 6-12. http://dx.doi.org/10.2316/Journal.202.2013.1.202-3246

[29]   A. H. Foruzan, Y. W. Chen, R. A. Zoroofi, A. Furukawa, Y. Sato, M. Hori, et al., “Segmentation of Liver in LowContrast Images Using K-Means Clustering and Geodesic Active Contour Algorithms,” IEICE Transactions on Information and Systems, Vol. E96-D, No. 4, 2013, pp. 798-807.

[30]   U. Kutbay and F. Hardalac, “CT Liver Tissue Segmentation Using Distance Regularized Level Set Evolution Based on Spatial Fuzzy Clustering,” Energy Education Science and Technology Part A: Energy Science and Research, Vol. 29, No. 2, 2012, pp. 715-720.

[31]   S. S. Kumar and R. S. Moni, “Diagnosis of Liver Tumour from CT Images Using Contourlet Transform,” International Journal of Biomedical Engineering and Technology, Vol. 7, No. 3, 2011, pp. 276-290. http://dx.doi.org/10.1504/IJBET.2011.043300

[32]   L. Ruskó, G. Bekes and M. Fidrich, “Automatic Segmentation of the Liver from Multiand Single-Phase Contrast-Enhanced CT Images,” Medical Image Analysis, Vol. 13, No. 6, 2009, pp. 871-882.
http://dx.doi.org/10.1016/j.media.2009.07.009

[33]   H. Jiang, B. He, Z. Ma, M. Zong, X. Zhou and H. Fujita, “Liver Segmentation Based on Snakes Model and Improved Growcut Algorithm in Abdominal CT Image,” Computational and Mathematical Methods in Medicine, Vol. 2013, 2013.

[34]   H. Jiang, Z. Ma and M. Zong, “Semi-Automatic Medical Image Segmentation Based on Improved Grow-Cut Algorithm,” Journal of Pure and Applied Microbiology, Vol. 7, 2013, pp. 453-459.

[35]   A. Afifi and T. Nakaguchi, “Liver Segmentation Approach Using Graph Cuts and Iteratively Estimated Shape and Intensity Constrains,” Medical Image Computing and Computer-Assisted Intervention, Vol. 15, No. 2, 2012, pp. 395-403.

[36]   R. Beichel, A. Bornik, C. Bauer and E. Sorantin, “Liver Segmentation in Contrast Enhanced CT Data Using Graph Cuts and Interactive 3D Segmentation Refinement Methods,” Medical Physics, Vol. 39, No. 3, 2012, pp. 1361-1373. http://dx.doi.org/10.1118/1.3682171

[37]   F. Kang and J. Yang, “An Improved Method for Medical Liver Segmentation and Real-Time Rendering,” Gaojishu Tongxin/Chinese High Technology Letters, Vol. 21, No. 11, 2011, pp. 1164-1170.

[38]   X. Gao, “A Level Set Image Segmentation Method Based on Prior Shape Statistical Knowledge,” International Review on Computers and Software, Vol. 7, No. 6, 2012, pp. 3221-3226.

[39]   A. Zidan, N. I. Ghali, A. E. Hassanien, H. Hefny and J. Hemanth, “Level Set-Based CT Liver Computer Aided Diagnosis System,” International Journal of Imaging and Robotics, Vol. 9, No. 1, 2012, pp. 26-36.

[40]   D. A. B. Oliveira, R. Q. Feitosa and M. M. Correia, “Segmentation of Liver, Its Vessels and Lesions from CT Images for Surgical Planning,” BioMedical Engineering OnLine, Vol. 10, 2011, p. 30. http://dx.doi.org/10.1186/1475-925X-10-30

[41]   L. Fernandez-de-Manuel, J. L. Rubio, M. J. LedesmaCarbayo, J. Pascau, J. M. Tellado, E. Ramon, M. Desco and A. Santos, “3D Liver Segmentation in Preoperative CT Images Using a Level-Sets Active Surface Method,” Conference Proceedings. IEEE Engineering in Medicine and Biology Society, Vol. 2009, 2009, pp. 3625-3628.

[42]   W. N. J. H. Wan Yussof and H. Burkhardt, “Automatic 3D Liver Segmentation Using Morphological Operations and Graph-Cut Techniques,” Journal of Next Generation Information Technology, Vol. 2, No. 3, 2011, pp. 23-34. http://dx.doi.org/10.4156/jnit.vol2.issue3.2

[43]   X. Zhang, T. Tajima, T. Kitagawa, M. Kanematsu, X. Zhou, T. Hara, et al., “An Automatic Method for Extracting the Liver Contour on Multi-Phase CT Images with Hepatic Lesions,” International Journal of Computer Assisted Radiology and Surgery, Vol. 1, Suppl. 7, 2006, pp. 69-71.

[44]   S. Tomoshige, E. Oost, A. Shimizu, H. Watanabe and S. Nawano, “A Conditional Statistical Shape Model with Integrated Error Estimation of the Conditions; Application to Liver Segmentation in Non-Contrast CT Images,” Medical Image Analysis, Vol. 18, No. 1, 2014, pp. 130-143.
http://dx.doi.org/10.1016/j.media.2013.10.003

[45]   S. T. Gollmer, M. Simon, A. Bischof, J. Barkhausen and T. M. Buzug, “Multi-Object Active Shape Model Construction for Abdomen Segmentation: Preliminary Results,” Conference Proceedings. IEEE Engineering in Medicine and Biology Society, Vol. 2012, 2012, pp. 3990-3993.

[46]   X. Wang, C. Zheng, C. Li, Y. Yin and D. D. Feng, “Automated CT Liver Segmentation Using Improved ChanVese Model with Global Shape Constrained Energy,” Conference Proceedings. IEEE Engineering in Medicine and Biology Society, Vol. 2011, 2011, pp. 3415-3418.

[47]   S. Zhang, Y. Zhan, M. Dewan, J. Huang, D. N. Metaxas and X. S. Zhou, “Deformable Segmentation via Sparse Shape Representation,” Medical Image Computing and Computer-Assisted Intervention, Vol. 14, Pt. 2, 2011, pp. 451-458.

[48]   X. Zhang, J. Tian, K. Deng, Y. Wu and X. Li, “Automatic Liver Segmentation Using a Statistical Shape Model with Optimal Surface Detection,” IEEE Transactions on Biomedical Engineering, Vol. 57, No. 10, 2010, pp. 2622-2626. http://dx.doi.org/10.1109/TBME.2010.2056369

[49]   X. F. Wang and Y. Q. Zhao, “Liver CT Image Segmentation Based on Prior Shape CV Model,” Guangdianzi Jiguang/Journal of Optoelectronics Laser, Vol. 21, No. 6, 2010, pp. 953-956.

[50]   A. H. Foruzan, R. Aghaeizadeh Zoroofi, M. Hori and Y. Sato, “Liver Segmentation by Intensity Analysis and Anatomical Information in Multi-Slice CT Images,” International Journal of Computer Assisted Radiology and Surgery, Vol. 4, No. 3, 2009, pp. 287-297. http://dx.doi.org/10.1007/s11548-009-0293-2

[51]   T. Heimann, S. Münzing, H. P. Meinzer and I. Wolf, “A Shape-Guided Deformable Model with Evolutionary Algorithm Initialization for 3D Soft Tissue Segmentation,” Information Processing in Medical Imaging (IPMI 2007), Lecture Notes in Computer Science, Vol. 4584, 2007, pp. 1-12.

[52]   T. Heimann, I. Wolf and H. P. Meinzer, “Active Shape Models for a Fully Automated 3D Segmentation of the Liver—An Evaluation on Clinical Data,” Medical Image Computing and Computer-Assisted Intervention, Vol. 4191, Pt. 2, 2006, pp. 41-48.

[53]   S. Wang, D. Fu, M. Xu and D. Hu, “Advanced Fuzzy Cellular Neural Network: Application to CT Liver Images,” Artificial Intelligence in Medicine, Vol. 39, No. 1, 2007, pp. 65-77.
http://dx.doi.org/10.1016/j.artmed.2006.08.001

[54]   S. A. Husain and E. Shigeru, “Use of Neural Networks for Feature Based Recognition of Liver Region on CT Images,” Proceedings of the IEEE Workshop on Neural Networks for Signal Processing, Vol. 2, 2000, pp. 831-840.

[55]   M. Goryawala, M. R. Guillen, S. Gulec, T. Barot, R. Suthar, R. Bhatt, A. Mcgron and M. Adjouadi, “An Accurate 3D Liver Segmentation Method for Selective Internal Radiation Therapy Using a Modified K-Means Algorithm and Parallel Computing,” International Journal of Innovative Computing Information and Control, Vol. 8, No. 10A, 2012, pp. 6515-6538.

[56]   P. Campadelli, E. Casiraghi and A. Esposito, “Liver Segmentation from Computed Tomography Scans: A Survey and a New Algorithm,” Artificial Intelligence in Medicine, Vol. 45, No. 2-3, 2009, pp. 185-196. http://dx.doi.org/10.1016/j.artmed.2008.07.020

[57]   W. Schneider, T. Bortfeld and W. Schlegel, “Correlation between CT Numbers and Tissue Parameters Needed for Monte Carlo Simulations of Clinical Dose Distributions,” Physics in Medicine & Biology, Vol. 45, No. 2, 2000, pp. 459-478. http://dx.doi.org/10.1088/0031-9155/45/2/314

[58]   D. Wolf, “Clinical Methods: The History, Physical, and Laboratory Examinations: Chapter 94: Evaluation of the Size, Shape, and Consistency of the Liver,” 1990.

 
 
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