Abstract
Hepatic diseases are serious condition worldwide, and several times doctors analyse the situation and elaborates a preoperative planning based exclusively on the medical images, which are a drawback since they only provide a 2D vision and the location of the damaged tissues in the three-dimensional space cannot be easily determined by surgeons. Nowadays, with the advancement of Computer Aided Design (CAD) technologies and image segmentation, a digital liver model can be obtained to help understand the particular medical case; even with the geometric model, a virtual simulation can be elaborated. This work is divided into two phases; the first phase involves a workflow to create a liver geometrical model from medical images. Whereas the second phase provides a methodology to achieve liver prototype, using the technique of fused deposition modelling (FDM). The two stages determine and evaluate the most influencing parameters to make this design repeatable in different hepatic diseases. The reported case study provides a valuable method for optimizing preoperative plans for liver disease. In addition, the prototype built with additive manufacturing will allow the new doctors to speed up their learning curve, since they can manipulate the real geometry of the patient's liver with their hands.
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References
U.S. National Library of Medicine, “MedlinePlus,” Liver Diseases (2019). https://medlineplus.gov/liverdiseases.html#cat_79. Accessed 11 Apr 2021
Billingsley, K.G., Jarnagin, W.R., Fong, Y., Blumgart, L.H.: Segment-oriented hepatic resection in the management of malignant neoplasms of the liver. J. Am. Coll. Surg. 187(5), 471–481 (1998)
McColl, R.J., Shaheen, A.A.M., Brar, B., Kaplan, G., Myers, R., Sutherland, F., Dixon, E.: Survival after hepatic resection: impact of surgeon training on long-term outcome. Can. J. Surg. 56(4), 256–262 (2013)
Catalano, O.A., Singh, A.H., Uppot, R.N., Hahn, P.F., Ferrone, C.R., Sahani, D.V.: Vascular and biliary variants in the liver: implications for liver surgery. Radiographics 28(2), 359–378 (2008)
Helling, T.S.: Liver failure following partial hepatectomy. HPB 8(3), 165–174 (2006)
Ikegami, T., Maehara, Y.: Transplantation: 3D printing of the liver in living donor liver transplantation. Nat. Rev. Gastroenterol. Hepatol. 10(12), 697–698 (2013)
Zein, N.N., et al.: Three-dimensional print of a liver for preoperative planning in living donor liver transplantation Liver Transplant. 19(12), 1304–1310
House, M.G., et al.: Survival after hepatic resection for metastatic colorectal cancer: trends in outcomes for 1,600 patients during two decades at a single institution. J. Am. Coll. Surg. 210(5), 744–752 (2010)
Guachi, L., Guachi, R., Bini, F., Marinozzi, F.: Automatic colorectal segmentation with convolutional neural network. Comput.-Aided Des. Appl. 16(5), 836–845 (2019)
Kim, S., Lim, H: Method of background subtraction for medical image segmentation, In: CITSA 2006 - 3rd International Conference on Cybernetics and Information Technology, Systems and Applications jointly with the 4th International Conference on Computing, Communications and Control Technologies, CCCT 2006 - Proceedings, vol. 1, pp. 87–91 (2006)
Gayathri Devi, K., Radhakrishnan, R.: Automatic segmentation of colon in 3D CT images and removal of opacified fluid using cascade feed forward neural network. Comput. Math. Meth. Med. 2015(670739), 1–15 (2015)
Kainz, P., Pfeiffer, M., Urschler, M.: Semantic segmentation of colon glands with deep convolutional neural networks and total variation segmentation. ArXiv, pp. 1–15 (2015)
Uccheddu, F., Carfagni, M., Governi, L., Furferi, R., Volpe, Y., Nocerino, E.: 3D printing of cardiac structures from medical images: an overview of methods and interactive tools. Int. J. Interact. Des. Manuf. 12(2), 597–609 (2018)
Madurska, M.J., Poyade, M., Eason, D., Rea, P., Watson, A.J.M.: Development of a patient-specific 3D-printed liver model for preoperative planning. Surg. Innov. 24(2), 145–150 (2017)
Bici, M., et al.: Digital design of medical replicas via desktop systems: shape evaluation of colon parts. J. Healthcare Eng. 2018(3272596), 1–10 (2018)
Guachi, R., Bici, M., Guachi, L., Campana, F., Bini, F., Marinozzi, F.: Geometrical modelling effects on FEA of colorectal surgery Comput.-Aided Des. Appl. 16(4), 778–788 2019
Guachi, R., Bini, F., Bici, M., Campana, F., Marinozzi, F., Guachi, L.: Finite element analysis in colorectal surgery: non-linear effects induced by material model and geometry. Comput. Meth. Biomech. Biomed. Eng. Imaging Visual. 8(2), 219–230 (2020)
Kühnapfel, U.G., Kuhn, C., Hübner, M., Krumm, H.-G., Maass, H., Neisius, B.: The Karlsruhe Endoscopic Surgery Trainer as an example for virtual reality in medical education. Minimally Invasive Therapy Allied Technol. 6(2), 122–125 (1997)
Ircad, 3D-IRCADb-01 database. https://www.ircad.fr/research/3d-ircadb-01/. Accessed 11 Apr 2021
Yu, H., Huang, T.-Z., Deng, L.-J., Zhao, X.-L.: Super-resolution via a fast deconvolution with kernel estimation. Eurasip J. Image Video Process. 2017(1), Article no. 3 (2016)
Christinal, H.A., Díaz-Pernil, D., Real Jurado, P.: Segmentation in 2D and 3D image using tissue-like P system. In: Bayro-Corrochano, E., Eklundh, J.-O. (eds.) CIARP 2009. LNCS, vol. 5856, pp. 169–176. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-10268-4_20
Vorontsov, E., Tang, A., Pal, C., Kadoury, S.: Liver lesion segmentation informed by joint liver segmentation. In: Proceedings - International Symposium on Biomedical Imaging, April 2018, pp. 1332–1335 (2018)
Yang, J., Dvornek, N.C., Zhang, F., Chapiro, J., Lin, M., Duncan, J.S.: Unsupervised domain adaptation via disentangled representations: application to cross-modality liver segmentation. In: Shen, D. et al. (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2019, MICCAI 2019. LNCS, vol. 11765, pp. 255–263. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_29
Bici, M., Campana, F., Petriaggi, S., Tito, L.: Study of a point cloud segmentation with part type recognition for tolerance inspection of plastic components via reverse engineering. Comput.-Aided Des. Appl. 11(6), 640–648 (2014)
Bici, M., Campana, F., Trifirò, A., Testani, C.: Development of automatic tolerance inspection through Reverse Engineering. In: 2014 IEEE International Workshop on Metrology for Aerospace, MetroAeroSpace 2014 - Proceedings, Article no. 6865903, pp. 107–112 (2014)
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Guachi, R. et al. (2022). 3D Printing of Prototypes Starting from Medical Imaging: A Liver Case Study. In: Rizzi, C., Campana, F., Bici, M., Gherardini, F., Ingrassia, T., Cicconi, P. (eds) Design Tools and Methods in Industrial Engineering II. ADM 2021. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-91234-5_54
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