Abstract
The advent of imaging methods in medicine has yielded new diagnosing dynamics inside hospitals. Since imaging allows the inspection with few or no intrusiveness, there is a remarked intention in producing medical verdicts from the radiology data by implementing computational algorithms and, therefore preclude the use of the long-lasting analytics that involve manual segmentation or often painful procedures such as histology. Currently, troves of medical-imaging data are stored in the picture archiving and communication system (PACS) – the standard imaging database –. The massive storage is initially created and maintained obeying to the legal regulations, but the resulting repository holds unbeatable conditions to apply artificial intelligence and derive conclusions from hidden patterns, a new mechanism never envisaged before. However, the same regulations that enabled the creation of the medical imaging repository have precluded the quantifications from images stored in PACS.
This paper presents a strategy that empowers PACS so that analytical procedures can run without violating confidentiality policies or creating security breaches. The platform supports unlimited analytical procedures, and, as a prof of concept, the problem of accurately measuring the maximum head circumference in pediatrics is solved and presented.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bellon, E., Feron, M., Deprez, T., Reynders, R., den Bosch, B.V.: Trends in PACS architecture. Eur. J. Radiol. 78, 199–204 (2011)
Bellon, E., et al.: Incorporating novel image processing methods in a hospital-wide PACS. Int. Congress Ser. 1281, 1016– 1021 (2005). https://doi.org/10.1016/j.ics.2005.03.210, www.ics-elsevier.com
Benkrid, K., Crookes, D., Benkrid, A.: Design and FPGA implementation of a perimeter estimator. In: Proceedings of the Irish Machine Vision and Image Processing Conference, pp. 51–57 (2000)
Bidgood, W.D., Horh, S.T., Prior, F.W., VanSyckle, D.E.: Understanding and using DICOM, the data interchange standard for biomedical imaging. J. Am. Med. Inform. Assoc. 4, 199–212 (1997)
Blood, R.: How bloggin software reshapes the online commnunity. Commun. ACM 14, 53–55 (2004)
Rollins, J.D., Collins, J.S., Holden, K.R.: United states head circumference growth reference charts birth to 21 years. J. Pediatr. 156(6), 907–13 (2010)
Reinstein, D.Z., Archer, T.J., Silverman, R.H., Coleman, D.J.: Accuracy, repeatability, and reproducibility of artemis very high-frequency digital ultrasound arc-scan lateral dimension measurements. J. Cataract. Refract. Surg. 32(11), 1799–1802 (2006). https://doi.org/10.1016/j.jcrs.2006.07.017
Gorgolewski, K., et al.: Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python. Front. Neuroinform. 5 (2011). https://doi.org/10.3389/fninf.2011.00013, http://dx.doi.org/10.3389/fninf.2011.00013
Gueld, M.O., et al.: Quality of DICOM header information for image categorization. In: Proceedings of SPIE, Medical Imaging, vol. 4685 (2002)
Huang, H.K.: Short history of PACS. Part I: USA. Eur. J. Radiol. 78(2), 163 – 176 (2011). https://doi.org/10.1016/j.ejrad.2010.05.007, http://dx.doi.org/10.1016/j.ejrad.2010.05.007
Jenkinson, M., Beckmann, C., Behrens, T., Woolrich, M., Smith, S.: FSL. Neuroimage 62, 782–90 (2012)
Chamberlain, J., Rogers, P., Price, J.L., Ginks, S., Nathan, B.E., Burn, I.: Validity of clinical examination and mammography as screening tests for breast cancer. The Lancet 306(7943), 1026–1030 (1975). https://doi.org/10.1016/S0140-6736(75)90304-9
Dickersin, K., Min, Y.I., Meinert, C.L.: Factors influencing publication of research results follow-up of applications submitted to two institutional review boards. JAMA 267(3) (1992). https://doi.org/jama.1992.03480030052036
Li, J., et al.: Automatic fetal head circumference measurement in ultrasound using random forest and fast ellipse fitting. IEEE J. Biomed. Health Inform. 22(1), 215–223 (2017)
Ma, D., Lin, F., Chua, C.K.: Rapid prototyping applications in medicine. Part 2: STL file generation and case studies. Int. J. Adv. Manuf. Technol. 18, 118–127 (2001)
Mahmoudi, S.E., et al.: Web-based interactive 2D/3D medical image processing and visualization software. Comput. Methods Programs Biomed. 98(2), 172 – 182 (2009). https://doi.org/10.1016/j.cmpb.2009.11.012, http://dx.doi.org/10.1016/j.cmpb.2009.11.012
de Onis, M.: WHO Child Growth Standards. Methods and Development. No. 978 92 4 154718 5, World Health Organization (2009). http://www.who.int/childgrowth/standards/velocity/tr3_velocity_report.pdf?ua=1
Perez, F., et al.: RADStation3G: a platform for cardiovascular image analysis integrating PACS, 3D+t visualization and grid computing. Comput. Methods Programs Biomed. 110(3), 399–410 (2012). https://doi.org/10.1016/j.cmpb.2012.12.002, http://dx.doi.org/10.1016/j.cmpb.2012.12.002
Qiao, L., et al.: Medical high-resolution image sharing and electronic whiteboard system: a pure-web-based system for accessing and discussing lossless original images in telemedicine. Comput. Methods Programs Biomed. 121(2), 77–91 (2015). https://doi.org/10.1016/j.cmpb.2015.05.010, http://dx.doi.org/10.1016/j.cmpb.2015.05.010
European Society of Radiology 2009: The future role of radiology in healthcare. Insights Imaging 1(1), 2–11 (2010). https://doi.org/10.1007/s13244-009-0007-x
Ratiba, O., Rosset, A.: Can PACS benefit from general consumer communicationtools? Int. Congress Ser. 1281, 948–953 (2005). https://doi.org/10.1016/j.ics.2005.03.344, www.ics-elsevier.com
Rodola, G.: Psutil package: a cross-platform library for retrieving information on running processes and system utilization (2016). https://pypi.python.org/pypi/psutil
Tieche, M., Gump, J., Rieck, M.E., Schneider., A.: This white paper explores the decade of PACS technology, changes, growth in numbers of vendors, and installations in hospitals in the United States. The Dorenfest Institute (2010)
Tollard, E., Darsaut, T., Bing, F., Guilbert, F., Gevry, F., Raymond, J.: Outcomes of endovascular treatments of aneurysms: observer variability and implications for interpreting case series and planning randomized trials. Am. J. Neuroradiol. 33(4) (2012). https://doi.org/10.3174/ajnr.A2848
Villar, J., et al., for the International Fetal, for the 21st Century (INTERGROWTH-21st), N.G.C.: International standards for newborn weight and length and head circumference by gestational age and sex the newborn and cross-sectional study and of the intergrowth-21st project. Lancet 384, 857–68 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Yepes Calderon, F., Rea, N., McComb, J.G. (2018). Enabling the Medical Applications Engine. In: Florez, H., Diaz, C., Chavarriaga, J. (eds) Applied Informatics. ICAI 2018. Communications in Computer and Information Science, vol 942. Springer, Cham. https://doi.org/10.1007/978-3-030-01535-0_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-01535-0_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01534-3
Online ISBN: 978-3-030-01535-0
eBook Packages: Computer ScienceComputer Science (R0)