Share and enjoy: anatomical models database—generating and sharing cardiovascular model data using web services
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Sharing data between scientists and with clinicians in cardiac research has been facilitated significantly by the use of web technologies. The potential of this technology has meant that information sharing has been routinely promoted through databases that have encouraged stakeholder participation in communities around these services. In this paper we discuss the Anatomical Model Database (AMDB) (Gianni et al. Functional imaging and modeling of the heart. Springer, Heidelberg, 2009; Gianni et al. Phil Trans Ser A Math Phys Eng Sci 368:3039–3056, 2010) which both facilitate a database-centric approach to collaboration, and also extends this framework with new capabilities for creating new mesh data. AMDB currently stores cardiac geometric models described in Gianni et al. (Functional imaging and modelling of the heart. Springer, Heidelberg, 2009), a number of additional cardiac models describing geometry and functional properties, and most recently models generated using a web service. The functional models represent data from simulations in geometric form, such as electrophysiology or mechanics, many of which are present in AMDB as part of a benchmark study. Finally, the heartgen service has been added for producing left or bi-ventricle models derived from binary image data using the methods described in Lamata et al. (Med Image Anal 15:801–813, 2011). The results can optionally be hosted on AMDB alongside other community-provided anatomical models. AMDB is, therefore, a unique database storing geometric data (rather than abstract models or image data) combined with a powerful web service for generating new geometric models.
KeywordsCortisol Catching efficiency DC electrofishing AC electrofishing Cast net Plecoglossus altivelis
We would like to acknowledge the contributors to AMDB for providing and processing legacy data: Oscar Camara and Alejandro Frangi of the Center for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB) at the Universitat Pompeu Fabra; Hervé Delingette, Maxime Sermesant and Nicholas Ayache of INRIA Sophia-Antipolis; Israel Valverde and Philipp Beerbaum of the Imaging Sciences and Biomedical Engineering Division, King’s College London; Cristina Staicu and Alistair Brown of the Department of Cardiovascular Science, University of Sheffield. This work was supported by the European Commission (FP7-ICT-224485:euHeart) and the authors would like to acknowledge the work of the whole euHeart consortium.
- 3.Christie GR, Nielsen P, Blackett S, Bradley C, Hunter P (2009) FieldML: concepts and implementation. Philos Transact A Math Phys Eng Sci 367:1869–1884Google Scholar
- 5.Ecabert O, Peters J, Schramm H, Lorenz C, von Berg J, Walker M, Vembar M, Olszewski M, Subramanyan K, Lavi G, Weese J (2008) Automatic model-based segmentation of the heart in ct images. IEEE Trans Med Imaging 27(9):1189–1201Google Scholar
- 6.Gianni D, McKeever S, Smith N (2009) euheartdb: a web-enabled database for geometrical models of the heart. In: Ayache N, Delingette H, Sermesant M (eds) Functional imaging and modeling of the heart, vol 5528 of Lecture Notes in Computer Science, pp 407–416. Springer, Berlin/HeidelbergGoogle Scholar
- 7.Gianni D, McKeever S, Yu T, Britten R, Delingette H, Frangi A, Hunter P, Smith N (2010) Sharing and reusing cardiovascular anatomical models over the Web: a step towards the implementation of the virtual physiological human project. Phil Trans Ser A Math Phys Eng Sci 368(1921):3039–3056CrossRefGoogle Scholar
- 9.Lamata P, Niederer S, Plank G, Smith N (2010) Generic conduction parameters for predicting activation waves in customised cardiac electrophysiology models. In: Proceedings of the First international conference on Statistical atlases and computational models of the heart, and international conference on Cardiac electrophysiological simulation challenge, STACOM’10/CESC’10, pp 252–260. Springer, Berlin/HeidelbergGoogle Scholar
- 10.Legrice I, Hunter P, Smaill B (1997) Laminar structure of the heart: a mathematical model. Am J Physiol Heart Circ Physiol 272(5):H2466–H2476Google Scholar
- 12.McCormick M, Nordsletten DA, Kay D, Smith NP (2012) Simulating left ventricular fluid-solid mechanics through the cardiac cycle under lvad support. J Comput Phys. doi: 10.1016/j.jcp.2012.08.008
- 13.Niederer S, Kerfoot E, Benson A, Bernabeu M, Bernus O, Bradley C, Cherry E, Clayton R, Fenton F, Garny A, Heidenreich E, Land S, Maleckar M, Pathmanathan P, Plank G, Rodríguez J, Roy I, Sachse F, Seemann G, Skavhaug O, Smith N (2011) Verification of cardiac tissue electrophysiology simulators using an n-version benchmark. Phil Trans R Soc A Math Phys Eng Sci 369(1954):4331–4351CrossRefGoogle Scholar
- 14.Ordas S, Oubel E, Sebastian R, Frangi AF (2007) Computational anatomy atlas of the heart. In: 5th international symposium on image and signal processing and analysis, ISPA 2007, IEEE, vol 8, pp 338–342Google Scholar
- 16.Smith NP, Crampin EJ, Niederer SA, Beard DA (2007) Computational biology of cardiac myocytes: proposed standards for the physiome. J Exp Biol 210:1576–1583Google Scholar
- 17.Spaan J, ter Wee R, van Teeffelen J, Streekstra G, Siebes M, Kolyva C, Vink H, Fokkema D, VanBavel E (2005) Visualisation of intramural coronary vasculature by an imaging cryomicrotome suggests compartmentalisation of myocardial perfusion areas. Med Biol Eng Comput 43:431–435. doi: 10.1007/BF02344722 Google Scholar