MammoGrid — a prototype distributed mammographic database for Europe

Warren, Ruth; Solomonides, Anthony; Frate, Chiara Del; Warsi, Iqbal; Ding, Jane; Odeh, Mohammed; McClatchey, Richard; Tromans, Christopher; Brady, Michael; Highnam, Ralph; Cordell, M.; Estrella, Florida; Bazzocchi, Massimo; Amendolia, S. R.

doi:10.1016/j.crad.2006.09.032

Cited by 36 publications

(27 citation statements)

References 9 publications

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“…Cancer cases include women whose unaffected breast will serve the density study to provide cases for the CADe analysis from the affected side mammogram. MammoGrid has demonstrated that these new forms of clinical collaboration can be supported using the Grid [26]. Furthermore a strong collaboration has been established through the evaluation phase of the project between radiologists active in breast cancer research and academic computer scientists with expertise in the applications of grid computing.…”

Section: Clinical Evaluation Of the Mammogrid Prototypementioning

confidence: 99%

Experiences of engineering Grid-based medical software

Estrella

Hauer

McClatchey

et al. 2007

International Journal of Medical Informatics

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SummaryObjectives: Grid-based technologies are emerging as potential solutions for managing and collaborating distributed resources in the biomedical domain. Few examples exist, however, of successful implementations of Grid-enabled medical systems and even fewer have been deployed for evaluation in practice. The objective of this paper is to evaluate the use in clinical practice of a Grid-based imaging prototype and to establish directions for engineering future medical Grid developments and their subsequent deployment. Method: The MammoGrid project has deployed a prototype system for clinicians using the Grid as its information infrastructure. To assist in the specification of the system requirements (and for the first time in healthgrid applications), use-case modelling has been carried out in close collaboration with clinicians and radiologists who had no prior experience of this modelling technique. A critical qualitative and, where possible, quantitative analysis of the MammoGrid prototype is presented leading to a set of recommendations from the delivery of the first deployed Grid-based medical imaging application. Results: We report critically on the application of software engineering techniques in the specification and implementation of the MammoGrid project and show that use-case modelling is a suitable vehicle for representing medical requirements and for communicating effectively with the clinical community. This paper also discusses the practical advantages and limitations of applying the Grid to real-life clinical applications and presents the consequent lessons learned. Conclusions:The work presented in this paper demonstrates that given suitable commitment from collaborating radiologists it is practical to deploy in practice medical imaging analysis applications using the Grid but that standardisation in and stability of the Grid software is a necessary pre-requisite for successful healthgrids. The MammoGrid prototype has therefore paved the way for further advanced Grid-based deployments in the medical and biomedical domains.

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Section: Clinical Evaluation Of the Mammogrid Prototypementioning

confidence: 99%

Experiences of engineering Grid-based medical software

Estrella

Hauer

McClatchey

et al. 2007

International Journal of Medical Informatics

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“…NeuGRID exploits the experience developed during the earlier FP5 MammoGrid project (this provided knowledge related to the middleware and upperware that allowed specific applications to 'talk' to the Grid while remaining independent) [43][44][45][46][47][48], and the AddNeuroMed study (the collection, archiving and retrieval of multicenter clinical data, biomedical images and computerized image analysis) from the FP6 InnoMed project.…”

Section: Neugrid's Gridmentioning

confidence: 99%

Grid Infrastructures for Computational Neuroscience: The neuGRID Example

et al. 2009

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Evolution of brain imaging in neurodegenerative diseasesBrain imaging was regarded as an elective examination in patients with cognitive decline 15 years ago [1]. The practice parameters for diagnosis and evaluation of dementia defined by the American Academy of Neurology regarded computed tomography (CT) and magnetic resonance (MR) as 'optional' assessments [2,3]. Over time, imaging in dementia has moved from a negative, exclusionary role to one that added positive diagnostic and prognostic information. In the late 1990s, the traditional exclusionary approach was abandoned in favor of the inclusive approach [4,5]. Rapid advances in neuroimaging technologies such as PET, single photon emission CT, MR spectroscopy, diffusion tensor imaging and functional MRI have offered new vision into the pathophysiology of Alzheimer's desease (AD) [6] and, consequently, increasingly new powerful data-analysis methods have been developed [7].Since the beginning of the 21st Century, the development of innovative techniques for region-of-interest-based volumetry, automated voxel-based morphometry, cortical thickness measurement, basal forebrain volumetry and multivariate statistics have emerged [7][8][9] and those measurements most feasible and accurate have started to be used in clinical settings. The availability to the neuroimaging community of large prospective image data repositories has led to the development of web-based interfaces to access data and online image analysis tools to assess longitudinal brain changes [10][11][12][13].With the development of novel analysis techniques, the computational complexity of neuroimaging analysis has also increased signifi cantly. Higher spatial resolution images and longer time scans are being acquired so that more voxels will need to be processed for each acquisition. The same applies to the computational resources required by algorithms, since these have become increasingly central processing Neuroscience is increasingly making use of statistical and mathematical tools to extract information from images of biological tissues. Computational neuroimaging tools require substantial computational resources and the increasing availability of large image datasets will further enhance this need. Many efforts have been directed towards creating brain image repositories including the recent US Alzheimer Disease Neuroimaging Initiative. Multisite-distributed computing infrastructures have been launched with the goal of fostering shared resources and facilitating data analysis in the study of neurodegenerative diseases. Currently, some Grid-and non-Grid-based projects are aiming to establish distributed e-infrastructures, interconnecting compatible imaging datasets and to supply neuroscientists with the most advanced information and communication technologies tools to study markers of Alzheimer's and other brain diseases, but they have so far failed to make a difference in the larger neuroscience community. NeuGRID is an Europeon comission-funded effort arising from the needs of the Alzheimer's...

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“…In 2002 the European Community funded projects specifically in the Health Care field: these are MammoGrid [8], and GEMSS [9]. The UK eScience program invested funding between 2001 and 2004 on Health Grid applications such as eDiaMonND [10], CLEF [11] and CareGrid [12].…”

Section: Related Work In Health Gridmentioning

confidence: 99%