Abstract
We propose an innovative tool for Pancreatic Ductal AdenoCarcinoma 3D reconstruction from Multi-Detector-Computed Tomography. The tumor mass is discriminated from health tissue, and the resulting segmentation labels are rendered preserving information on different hypodensity levels. The final 3D virtual model includes also pancreas and main peri-pancreatic vessels, and it is suitable for 3D printing. We performed a preliminary evaluation of the tool effectiveness presenting ten cases of Pancreatic Ductal AdenoCarcinoma processed with the tool to an expert radiologist who can correct the result of the discrimination. In seven of ten cases, the 3D reconstruction is accepted without any modification, while in three cases, only 1.88, 5.13, and 5.70 %, respectively, of the segmentation labels are modified, preliminary proving the high effectiveness of the tool.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
American Cancer Society (2016) Cancer facts and figures 2016. American Cancer Society, Atlanta
Shaib YH, Davila JA, El-Serag HB (2006) The epidemiology of pancreatic cancer in the united states: changes below the surface. Aliment Pharmacol Ther 24:87–94
Jemal A, Siegel R, Xu J, Ward E (2010) Cancer statistics. CA Cancer J Clin 60:277–300
Li D, Xie K, Wolff R, Abbruzzese JL (2004) Pancreatic cancer. Lancet 363(9414):1049–1057
Worthington TR, Williamson RCN (1999) The continuing challenge of exocrine pancreatic cancer. Comp Ther 25(6/7):360–365
Sa Cunha A, Rault A, Laurent C, Adhoute X, Vendrely V, Béllannée G, Brunet R, Collet D, Masson B (2005) Surgical resection after radiochemotherapy in patients with unresectable adenocarcinoma of the pancreas. J Am Coll Surg 201:359–365
Yushkevich PA, Piven J, Cody Hazlett H, Gimpel Smith R, Ho S, Gee JC, Gerig G (2006) User-guided 3d active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31:1116–1128
ITK-Snap web-site. http://www.itksnap.org. Accessed Apr 15 2016
Ferrari V, Cappelli C, Megali G, Pietrabissa A (2008) An anatomy driven approach for generation of 3d models from multi-phase ct images. Proc Int Congress Exhib IJCARS 3(Supplement):1
Dance DR, Christofides S, Maidment ADA, McLean ID, Ng KH (2014) Computed tomography. In: Diagnostic radiology physics. A handbook for teachers and students international atomic energy agency. International Atomic Energy Agency, Vienna, pp 259–261
Rogowska J (2000) Overview and fundamentals of medical image segmentation. Handbook of medical imaging. Processing and analysis. Academic Press, San Diego, pp 69–85
Marconi S, Auricchio F, Pietrabissa A (2012) 3D Virtual and physical pancreas reconstruction discriminating between health and tumor tissue with fuzzy logic. Int J CARS 7(Suppl 1):S71–S88
Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353
Sutton MA, Bezdek JC, Cahoon TC (2000) Image segmentation by fuzzy clustering: methods and issues. In: Bankman IN (ed) Handbook of medical imaging. Processing and analysis. Academic Press, San Diego, pp 87–106
Zadeh LA (1975) Fuzzy logic and approximate reasoning. Synthese 30:407–428
Shimizu A, Kimoto T, Furukawa D, Kobatake H, Nawano S, Shinozaki K (2008) Pancreas segmentation in three phase abdominal ct volume data. Int J CARS 3(Suppl 1):S188–S198
Shimizu A, Kimoto T, Kobatake H, Nawano S, Shinozaki K (2009) Patient specific atlas-guided pancreas segmentation from three-dimensional contrast-enhanced computed tomography. Int J CARS 4(Suppl 1):S29–S53
Shimizu A, Kimoto T, Kobatake H, Nawano S, Shinozaki K (2010) Automated pancreas segmentation from three-dimensional contrast-enhanced computed tomography. Int J CARS 5:85–98
Erdt M, Kirschner M, Drechsler K, Wesarg S, Hammon M, Cavallaro A (2011) Automatic pancreas segmentation in contrast enhanced ct data using learned spatial anatomy and texture descriptors, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp 2076–2082
Hammon M, Cavallaro A, Erdt M, Dankerl P, Kirschner M, Drechsler K, Wesarg S, Uder M, Janka R (2013) Model-based pancreas segmentation in portal venous phase contrast-enhanced CT images. J Digit Imaging 26:1082–1090
Jiang H, Tan H, Fujita H (2013) A hybrid method for pancreas extraction from CT image based on level set methods. Comput Math Methods Med. doi:10.1155/2013/479516
Tong T, Wolz R, Wang Z, Gao Q, Misawa K, Fujiwara M, Mori K, Hajnal JV, Rueckert D (2015) Discriminative dictionary learning for abdominal multi-organ segmentation. Med Image Anal 23:92–104
Okada T, Linguraru MG, Hori M, Suzuki Y, Summers RM, Tomiyama N, Sato Y (2012) Multi-organ segmentation in abdominal CT images. In: 34th Annual international conference of the IEEE EMBS
Okada T, Linguraru MG, Hori M, Summers RM, Tomiyama N, Sato Y (2015) Abdominal multi-organ segmentation from CT images using conditional shape—location and unsupervised intensity priors. Med Image Anal 26:1–18
Acknowledgments
The presented activity is inserted in the framework of 3D@UniPV project (http://www.unipv.it/3d), one of the strategic research area of the University of Pavia.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Research involving human participants and/or animals
This article does not contain any studies with human participants or animals performed by any of the authors.
Informed consent
For this type of study formal consent is not required.
Rights and permissions
About this article
Cite this article
Marconi, S., Pugliese, L., Del Chiaro, M. et al. An innovative strategy for the identification and 3D reconstruction of pancreatic cancer from CT images. Updates Surg 68, 273–278 (2016). https://doi.org/10.1007/s13304-016-0394-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13304-016-0394-8