Content-Based Organisation of Virtual Repositories of DICOM Objects

The integration of multi-centre medical image data to create knowledge repositories for research and training activities has been an aim targeted since long ago. This paper presents an environment to share, to process and to organise medical imaging data according to a structured framework in which the image reports play a key role. This environment has been validated on a clinical environment, facing problems such as firewalls and security restrictions, in the frame of the CVIMO (Valencian Cyberinfrastructure of Medical Imaging in Oncology) project. The environment uses a middleware called TRENCADIS (Towards a Grid Environment for Processing and Sharing DICOM Objects) that provides users with the management of multiple administrative domains, data encryption and decryption on the fly and semantic indexation of images. Data is structured into four levels: Global data available, virtual federated storages of studies shared across a vertical domain, subsets for projects or experiments on the virtual storage and individual searches on these subsets. This structure of levels gives the needed flexibility for organising authorisation, and hides data that are not relevant for a given experiment. The main components and interactions are shown in the document, outlining the workflows and explaining the different approaches considered, including the protocols used and the difficulties met. © 2009 Elsevier B.V. All rights reserved.The authors wish to thanks the financial support received from Valencia Region Ministry of Enterprises, University (Conselleria de Empresa, Universidad y Ciencia) to develop the project "Ciberinfraestructura Valenciana de Imagen medica Oncologica", with reference GVEMP06/04.Blanquer Espert, I.; Hernández García, V.; Meseguer Anastasio, JE.; Segrelles Quilis, JD. (2009). Content-Based Organisation of Virtual Repositories of DICOM Objects. Future Generation Computer Systems. 25(6):627-637. https://doi.org/10.1016/j.future.2008.12.004S62763725

An OGSA Middleware for Managing Medical Images Using Ontologies

The final publication is available at Springer via http://dx.doi.org/ 10.1007/s10877-005-0675-0This article presents a Middleware based on Grid Technologies that addresses the problem of sharing, transferring and processing DICOM medical images in a distributed environment using an ontological schema to create virtual communities and to define common targets. It defines a distributed storage that builds-up virtual repositories integrating different individual image repositories providing global searching, progressive transmission, automatic encryption and pseudo-anonimisation and a link to remote processing services. Users from a Virtual Organisation can share the cases that are relevant for their communities or research areas, epidemiological studies or even deeper analysis of complex individual cases. Software architecture has been defined for solving the problems that has been exposed before. Briefly, the architecture comprises five layers (from the more physical layer to the more logical layer) based in Grid Thecnologies. The lowest level layers (Core Middleware Layer and Server Services layer) are composed of Grid Services that implement the global managing of resources. The Middleware Components Layer provides a transparent view of the Grid environment and it has been the main objective of this work. Finally, the upest layer (the Application Layer) comprises the applications, and a simple application has been implemented for testing the components developed in the Components Middleware Layer. Other side-results of this work are the services developed in the Middleware Components Layer for managing DICOM images, creating virtual DICOM storages, progressive transmission, automatic encryption and pseudo-anonimisation depending on the ontologies. Other results, such as the Grid Services developed in the lowest layers, are also described in this article. Finally a brief performance analysis and several snapshots from the applications developed are shown. The performance analysis proves that the components developed in this work provide image processing applications with new possibilities for large-scale sharing, management and processing of DICOM images. The results show that the components fulfil the objectives proposed. The extensibility of the system is achieved by the use of open methods and protocols, so new components can be easily added.Blanquer Espert, I.; Hernández García, V.; Segrelles Quilis, JD. (2005). An OGSA Middleware for Managing Medical Images Using Ontologies. Journal of Clinical Monitoring and Computing. 19:295-305. doi:10.1007/s10877-005-0675-0S29530519“European DataGrid Project”. http://www.eu-datagrid.org.“Biomedical Informatics Research”. http://www.nbirn.net/.“ACI project MEDIGRID: medical data storage and processing on the GRID”.http://www.creatis.insa-lyon.fr/MEDIGRID/.“Information eXtraction from Images (IXI) Grid Services for Medical Imaging”. Working Notes of the Workshop on Distributed Databases and processing in Medical Image Computing (DIDAMIC'04). Pag 65.“NeuroBase: Management of Distributed and Heterogeneous Information Sources in Neuroimaging”. Working Notes of the Workshop on Distributed Databases and processing in Medical Image Computing (DIDAMIC'04). Pag 85.Digital Imaging and Communications in Medicine (DICOM) Part 10: Media Storage and File Format for Media Interchange. National Electrical Manufacturers Association, 1300 N. 17th Street, Rosslyn, Virginia 22209 USA.“Open Grid Services Architecture (OGSA)”, http://www.globus.org/ogsa.Globus alliance Home Page. “Relevant documents”, http://www.globus.orgAllen Wyke R, Watt A, “XML Schema Essentials”. Wiley Computer Pub. ISBN 0-471-412597Web security and commerce/Simson Garfinkel. - Cambridge: O'Reilly, 1997. - 483 p.; 23 cm. ISBN 1565922697“The GridFTP Protocol and Software”. http://www-fp.globus.org/datagrid/gridftp.html.JPEG2000: Image compression fundamentals, standards and practice/David S. Taubman, Michael W. Marcellin. – Boston [etc.] : Kluwer Academic, cop. 2002. - XIX, 773 p.; 24 cm. + 1 CD-Rom - (The Kluwer international series in engineering and computer science) ISBN 079237519XBradley J, Erickson MD, “Irreversible Compression of Medical Images”, Dpt. Radiology, Mayo F., Rochester, MN, Jo. of D. Imaging, DOI: 10.1007/s10278-002-0001-z, 02.Monitoring & Discovery System (MDS)” http://www-unix.globus.org/toolkit/mds/“Key management for encrypted data storage in distributed systems”. Proceedings of HeathGrid 2004

Improving knowledge management through the support of image examination and data annotation using DICOM structured reporting

[EN] An important effort has been invested on improving the image diagnosis process in different medical areas using information technologies. The field of medical imaging involves two main data types: medical imaging and reports. Developments based on the DICOM standard have demonstrated to be a convenient and widespread solution among the medical community. The main objective of this work is to design a Web application prototype that will be able to improve diagnosis and follow-on of breast cancer patients. It is based on TRENCADIS middleware, which provides a knowledge-oriented storage model composed by federated repositories of DICOM image studies and DICOM-SR medical reports. The full structure and contents of the diagnosis reports are used as metadata for indexing images. The TRENCADIS infrastructure takes full advantage of Grid technologies by deploying multi-resource grid services that enable multiple views (reports schemes) of the knowledge database. The paper presents a real deployment of such Web application prototype in the Dr. Peset Hospital providing radiologists with a tool to create, store and search diagnostic reports based on breast cancer explorations (mammography, magnetic resonance, ultrasound, pre-surgery biopsy and post-surgery biopsy), improving support for diagnostics decisions. A technical details for use cases (outlining enhanced multi-resource grid services communication and processing steps) and interactions between actors and the deployed prototype are described. As a result, information is more structured, the logic is clearer, network messages have been reduced and, in general, the system is more resistant to failures.The authors wish to thank the financial support received from The Spanish Ministry of Education and Science to develop the project "CodeCloud", with reference TIN2010-17804.Salavert Torres, J.; Segrelles Quilis, JD.; Blanquer Espert, I.; Hernández García, V. (2012). Improving knowledge management through the support of image examination and data annotation using DICOM structured reporting. Journal of Biomedical Informatics. 45(6):1066-1074. https://doi.org/10.1016/j.jbi.2012.07.004S1066107445

Exchanging Data for Breast Cancer Diagnosis on Heterogeneous Grid Platforms

This article describes the process of defining and implementing new components to exchange data between two real GRID-based platforms for breast cancer diagnosis. This highly collaborative work in development phase pretends to allow communication between middleware, namely TRENCADIS and DRI, in different virtual organizations. On the one hand, TRENCADIS is a Service-Oriented Architecture in which the usage of resources is represented with Grid services based on the Open Grid Service Architecture specification (OGSA).On the other hand, DRI is a software platform aimed at reducing the cost of hosting digital repositories of arbitrary nature on Grid infrastructures. TRENCADIS has been deployed in the Dr. Peset Hospital (Valencia, Spain) and DRI has been deployed in the S ao Jo ao Hospital (Porto, Portugal). The final objective of this work in progress is to share medical images and its associated metadata among geographically distributed research institutions, while maintaining confidentiality and privacy of data

MOSAIC roadmap for mobile collaborative work related to health and wellbeing.

The objective of the MOSAIC project is to accelerate innovation in Mobile Worker Support Environments. For that purpose MOSAIC develops visions and illustrative scenarios for future collaborative workspaces involving mobile and location-aware working. Analysis of the scenarios is input to the process of road mapping with the purpose of developing strategies for R&D leading to deployment of innovative mobile work technologies and applications across different domains. One of the application domains where MOSAIC is active is health and wellbeing. This paper builds on another paper submitted to this same conference, which presents and discusses health care and wellbeing specific scenarios. The aim is to present an early form of a roadmap for validation

A Systematic Approach for Using DICOM Structured Reports in Clinical Processes: Focus on Breast Cancer

The final publication is available at Springer via http://dx.doi.org/10.1007/s10278-014-9728-6.This paper describes a methodology for redesigning the clinical processes to manage diagnosis, follow-up, and response to treatment episodes of breast cancer. This methodology includes three fundamental elements: (1) identification of similar and contrasting cases that may be of clinical relevance based upon a target study, (2) codification of reports with standard medical terminologies, and (3) linking and indexing the structured reports obtained with different techniques in a common system. The combination of these elements should lead to improvements in the clinical management of breast cancer patients. The motivation for this work is the adaptation of the clinical processes for breast cancer created by the Valencian Community health authorities to the new techniques available for data processing. To achieve this adaptation, it was necessary to design nine Digital Imaging and Communications in Medicine (DICOM) structured report templates: six diagnosis templates and three summary templates that combine reports from clinical episodes. A prototype system is also described that links the lesion to the reports. Preliminary tests of the prototype have shown that the interoperability among the report templates allows correlating parameters from different reports. Further work is in progress to improve the methodology in order that it can be applied to clinical practice.We thank the subject matter experts for sharing their insights through this study. We are especially appreciative of the efforts of the Radiology Unit and Medical Oncology Unit teams at the University Hospital Dr. Peset. This work was partially supported by the Vicerectorat d'Investigacio de la Universitat Politecnica de Valencia (UPVLC) to develop the project "Mejora del proceso diagnostico del cancer de mama" with reference UPV-FE-2013-8.Medina, R.; Torres Serrano, E.; Segrelles Quilis, JD.; Blanquer Espert, I.; Martí Bonmatí, L.; Almenar-Cubells, D. (2015). A Systematic Approach for Using DICOM Structured Reports in Clinical Processes: Focus on Breast Cancer. Journal of Digital Imaging. 28(2):132-145. doi:10.1007/s10278-014-9728-6S132145282Ratib O: Imaging informatics: From image management to image navigation. Yearb Med Inform 2009; 167–172Oakley J. Digital Imaging: A Primer for Radiographers, Radiologists and Health Care Professionals. Cambridge University Press, 2003.Prokosch HU, Dudeck J: Hospital information systems: Design and development characteristics, impact and future architecture. Elsevier health sciences, 1995Foster I, Kesselman C, Tuecke S. 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Computing human image annotation. Conf Proc IEEE Eng Med Biol Soc 2009; 1:7065–8.Sittig DF, Wright A, Osheroff JA, et al. Grand challenges in clinical decision support. J Biomed Inform 2008; 41(2):387–392.Wagholikar KB, Sundararajan V, Deshpande AW. Modeling paradigms for medical diagnostic decision support: a survey and future directions. J Med Syst 2012; 36(5):3029–3049.Rubin DL. Creating and curating a terminology for radiology: Ontology modeling and analysis. J Digit Imaging 2008; 21(4):355–362.Kahn CE, Jr., Langlotz CP, Burnside ES, Carrino JA, Channin DS, Hovsepian DM, et al. Toward best practices in radiology reporting. Radiology 2009; 252(3):852–856.Taira PK, Soderlang SG, JAbovits RM. Automatic structuring of radiology free-text reports. Radiographics 2001; 21(1); 237–245.Fujii H, Yamagishi H, Ando Y, Tsukamoto N, Kawaguchi O, Kasamatsu T, et al. Structuring of free-text diagnostic report. Stud. Health Technol. Inform. 2007; 129: 669–673.Murff HJ, FitzHenry F, Matheny ME, Gentry N, Kotter KL, Crimin K, Dittus RS, Rosen AK, Elkin PL, Brown SH, Speroff T. Automated identification of postoperative complications within an electronic medical record using natural language processing. JAMA 2011; 306(8):848–855.Clunie DA: DICOM structured reporting. PixelMed Publishing, 2000D’Avolio LW, Nguyen TM, Farwell WR, Chen Y, Fitzmeyer F, Harris OM, Fiore LD. Evaluation of a generalizable approach to clinical information retrieval using the automated retrieval console (ARC). J Am Med Inform Assoc 2012; 17:375–382.Napel SA, Beaulieu CF, Redriguez C, Cui J, Xu J, Grupta A, et al. Automated retrieval of CT images of liver lesions on the basis of image similarity: Method and preliminary results. Radiology 2010; 256(1): 243–252.Langlotz CP. RadLex: A new method for indexing online educational materials. Radiographics 2006; 26(6):1595–1597.Crestania F, Vegas J, de la Fuente P. A graphical user interface for the retrieval of hierarchically structured documents. Inf Process Manag 2004; 40(2):269–289.Weiss DL, Langlotz CP. Structured reporting: Patient care enhancement or productivity nightmare? Radiology 2008. 249(3):739–747.Yen PY, Bakken S. Review of health information technology usability study methodologies. J Am Med Inform Assoc 2012; 19(3):413–422.Patrick R, Julien G, Christian L, Antoine G. Automatic medical encoding with SNOMED categories. BMC Med Inform Decis Mak 2008; 8(Suppl 1): S1–S6.Lopez-Garcia P, Boeker M, Illarramendi A, Schulz S. Usability-driven pruning of large ontologies: The case of SNOMED CT, J Am Med Inform Assoc 2012; 19:e102-e109.World Health Organization. International Statistical Classification of Diseases and Related Health Problems 10th Revision. http://apps.who.int/classifications/apps/icd/icd10online/ (accessed 29 Jan 2013)American College of Radiology (ACR) Breast Imaging Reporting and Data System Atlas (BI-RADS® Atlas)World Health Organization. International Classification of Diseases for Oncology, 3rd Edition (ICD-O-3). http://www.who.int/classifications/icd/adaptations/oncology/en/index.html (accessed 29 Jan 2013)Greene FL. TNM: Our language of cancer. CA Cancer J Clin 2004; 54(3):129–130.American Joint Committee of Cancer (AJCC). AJCC Cancer Staging Manual. Seventh Edition. Springer, 2010Hussein R, Engelmann U, Schroeter A, Meinzer HP. DICOM structured reporting: Part 1. Overview and characteristics, Radiographics 2004; 24(3):891–896.Sluis D, Lee KP, Mankovich N. DICOM SR - integrating structured data into clinical information systems. Medicamundi 2002; 46(2):31–36.Percha B, Nassif H, Lipson J, Burnside E, Rubin D. Automatic classification of mammography reports by BI-RADS breast tissue composition class. J Am Med Inform Assoc 2012; 19(5):913–916.Ciatto S, Houssami N, Apruzzese A, Bassetti E, Brancato B, Carozzi F, Catarzi S, Lamberini MP, Marcelli G, Pellizzoni R, Pesce B, Risso G, Russo F, Scorsolini A. Reader variability in reporting breast imaging according to BI-RADS assessment categories (the Florence experience). Breast 2006; 15(1):44–51.National Electrical Manufacturers Association (NEMA). Digital Imaging and Communications in Medicine (DICOM). Part 16: Content Mapping Resource. http://medical.nema.org/dicom/2004/04_16PU.PDF (accessed 29 Jan 2013)Dolin RH, Alschuler L, Boyer S, Beebe C, Behlen FM, Biron PV, Shvo AS. HL7 clinical document architecture, release 2. J Am Med Inform Assoc 2006; 13:30–39.Blanquer I, Hernández V, Meseguer JE, Segrelles D. Content-based organisation of virtual repositories of DICOM objects. Future Gener Comput Syst 2009; 25(6):627–637.Blanquer I, Hernández V, Segrelles D, Torres E. Enhancing privacy and authorization control scalability in the grid through ontologies. IEEE Trans Inf Technol Biomed 2009; 12(1):16–24.Salavert J, Maestre C, Segrelles D, Blanquer I, Hernández V, Medina R, Martí L: Grid prototype to support cancer of breast diagnostics in clinic practice. Proc of the 4th. Iberian Grid Infrastructure Conf. Netbiblo, 2010Segrelles D, Franco JM, Medina R, Blanquer I, Salavert J, Hernandez V, Martí L, Díaz G, Ramos R, Guevara MA, González N, Loureiro J, Ramos I. Exchanging data for breast cancer diagnosis on heterogeneous grid platforms. Computing and Informatics 2012; 31(1):3–15.Ali MS, Consens M, Lalmas M. Extended structural relevance framework: A framework for evaluating structured document retrieval. Inf Retrieval 2012; 15:558–590.Welter P, Riesmeier J, Fischer B, Grouls C, Kuhl C, Deserno, TM. 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Enhancing Privacy and Authorization Control Scalability in the Grid through Ontologies

© 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The use of data Grids for sharing relevant data has proven to be successful in many research disciplines. However, the use of these environments when personal data are involved (such as in health) is reduced due to its lack of trust. There are many approaches that provide encrypted storages and key shares to prevent the access from unauthorized users. However, these approaches are additional layers that should be managed along with the authorization policies. We present in this paper a privacy-enhancing technique that uses encryption and relates to the structure of the data and their organizations, providing a natural way to propagate authorization and also a framework that fits with many use cases. The paper describes the architecture and processes, and also shows results obtained in a medical imaging platform.Manuscript received November 19, 2007; revised July 27, 2008. First published August 4,2008; cur-rent version published January 4,2009. This work was supported in part by the Spanish Ministry of Education and Science to develop the project "ngGrid-New Generation Components for the Efficient Exploitation of eScience Infrastructures," under Grant TIN2006-12860 and in part by the Structural Funds of the European Regional Development Fund (ERDF).Blanquer Espert, I.; Hernández García, V.; Segrelles Quilis, JD.; Torres Serrano, E. (2009). Enhancing Privacy and Authorization Control Scalability in the Grid through Ontologies. IEEE Transactions on Information Technology in Biomedicine. 13(1):16-24. https://doi.org/10.1109/TITB.2008.2003369S162413

Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools

The vast amount of data produced by today's medical imaging systems has led medical professionals to turn to novel technologies in order to efficiently handle their data and exploit the rich information present in them. In this context, artificial intelligence (AI) is emerging as one of the most prominent solutions, promising to revolutionise every day clinical practice and medical research. The pillar supporting the development of reliable and robust AI algorithms is the appropriate preparation of the medical images to be used by the AI-driven solutions. Here, we provide a comprehensive guide for the necessary steps to prepare medical images prior to developing or applying AI algorithms. The main steps involved in a typical medical image preparation pipeline include: (i) image acquisition at clinical sites, (ii) image de-identification to remove personal information and protect patient privacy, (iii) data curation to control for image and associated information quality, (iv) image storage, and (v) image annotation. There exists a plethora of open access tools to perform each of the aforementioned tasks and are hereby reviewed. Furthermore, we detail medical image repositories covering different organs and diseases. Such repositories are constantly increasing and enriched with the advent of big data. Lastly, we offer directions for future work in this rapidly evolving field

Comparative study of healthcare messaging standards for interoperability in ehealth systems

Advances in the information and communication technology have created the field of "health informatics," which amalgamates healthcare, information technology and business. The use of information systems in healthcare organisations dates back to 1960s, however the use of technology for healthcare records, referred to as Electronic Medical Records (EMR), management has surged since 1990’s (Net-Health, 2017) due to advancements the internet and web technologies. Electronic Medical Records (EMR) and sometimes referred to as Personal Health Record (PHR) contains the patient’s medical history, allergy information, immunisation status, medication, radiology images and other medically related billing information that is relevant. There are a number of benefits for healthcare industry when sharing these data recorded in EMR and PHR systems between medical institutions (AbuKhousa et al., 2012). These benefits include convenience for patients and clinicians, cost-effective healthcare solutions, high quality of care, resolving the resource shortage and collecting a large volume of data for research and educational needs. My Health Record (MyHR) is a major project funded by the Australian government, which aims to have all data relating to health of the Australian population stored in digital format, allowing clinicians to have access to patient data at the point of care. Prior to 2015, MyHR was known as Personally Controlled Electronic Health Record (PCEHR). Though the Australian government took consistent initiatives there is a significant delay (Pearce and Haikerwal, 2010) in implementing eHealth projects and related services. While this delay is caused by many factors, interoperability is identified as the main problem (Benson and Grieve, 2016c) which is resisting this project delivery. To discover the current interoperability challenges in the Australian healthcare industry, this comparative study is conducted on Health Level 7 (HL7) messaging models such as HL7 V2, V3 and FHIR (Fast Healthcare Interoperability Resources). In this study, interoperability, security and privacy are main elements compared. In addition, a case study conducted in the NSW Hospitals to understand the popularity in usage of health messaging standards was utilised to understand the extent of use of messaging standards in healthcare sector. Predominantly, the project used the comparative study method on different HL7 (Health Level Seven) messages and derived the right messaging standard which is suitable to cover the interoperability, security and privacy requirements of electronic health record. The issues related to practical implementations, change over and training requirements for healthcare professionals are also discussed

Mecanismo de gestão de áreas de utilizador para repositórios de imagem médica

Mestrado em Engenharia de Computadores e TelemáticaA imagem médica em formato digital é um elemento presente nas mais variadas instituições prestadoras de cuidados de saúde, afirmando-se como um imprescindível elemento de suporte ao diagnóstico e terapêutica médica. Nesta área, os formatos e processos de armazenamento e transmissão são definidos pela norma internacional DICOM. Um ficheiro deste tipo contempla, para além da imagem (ou vídeo), um conjunto de meta-dados que incluem informação dos pacientes, dados técnicos relativos ao estudo, dose de radiação, relatório clínico, etc. Um dos maiores problemas associados aos repositórios de imagem médica está relacionado com a grande quantidade de dados produzidos que impõe desafios acrescidos ao armazenamento e transporte da informação, em particular em cenários distribuídos e de grande produção de estudos imagiológicos. Esta dissertação tem como objetivo estudar e explorar soluções que permitam a integração do conceito de pertença e controlo de acesso em arquivos de imagem médica, possibilitando a centralização de múltiplas instâncias de arquivos. A solução desenvolvida permite associar permissões a recursos e delegação a terceiras entidades. Foi desenvolvida uma interface programática de gestão da solução proposta, disponibilizada através de web services, com a capacidade de criação, leitura, atualização e remoção de todos os componentes resultantes da arquitetura.The production of medical images in digital format has been growing in the most varied health care providers, representing at this moment an important and indispensable element for supporting medical decisions. In medical imaging area, the formats and transmission processes are defined by the international DICOM standard. A file in this format contains image pixel data but also a set of metadata, including information about the patient, technical data related to the study, dose of radiation, clinical report, etc. One of the biggest problems associated with medical imaging repositories is related to the large amount of data produced that poses additional challenges to the transport and archive of information, particularly in distributed environments and laboratories with huge volume of examinations. This dissertation aims to study and explore solutions for the integration of ownership concept and access control over medical imaging resources, making possible the centralization of multiple instances of repositories. The proposed solution allows the association of permissions to repository resources and delegation of rights to third entities. It was developed a programmatic interface for management of proposed services, made available through web services, with the ability to create, read, update and remove all components resulting from the architecture