Medical imaging enables non-invasive observation of living tissue and it is widely used for clinical care and research. A variety of acquisition modalities (CT, MRI, etc) generate images that are increasingly detailed, large and complex. The challenges for data management and analysis are also increasingly large, and distributed infrastructures such as grids and clouds have been adopted to address them.
In the Netherlands the first attempts to run for medical image analysis on the Dutch Grid were done by the Medical Imaging and Diagnosis program of the Virtual Laboratory for e-Science project (VL-e). Currently these efforts continue in various academic hospitals and universities.
e-BioGrid is open to take more Medical Imaging dedicated projects on board. Contact us if you are involved in Medical Imaging and you need support in software or hardware infrastructure.
The e-infrastructure for bioscience research, e-bioinfra, is routinely used by researchers at the AMC to perform medical image analysis on the Dutch Grid. The image analysis pipelines are implemented as workflows that are executed on the grid in an automated fashion. Various neuroimaging applications have been ported to this platform and made available for researchers from the Radiology, Psychiatry and other clinical departments at the AMC. The web interface of the e-bioinfra gateway provides easy access to novice users to applications such as FreeSurfer (brain surface segmentation) and DTI atlas construction. The goal of the project is to enable and enhance medical imaging research via advanced tools for data analysis. This is achieved in close collaboration with medical imaging researchers.
Silvia Olabarriaga, on behalf of the VLEMED VO, Amsterdam Medical Centre / University of Amsterdam
Infrastructure developed. The AMC now operates a WS-PGRADE science gateway in addition to the in-house developed gateway. See http://www.ebioscience.amc.nl/liferay-portal-6.1.0/. Support was provided for the installation of the gateway using grid resources using the e-infrastructure gateway at AMC, the construction of the first workflows, and internal training.
Knowledge. The AMC participated in an international collaboration to develop a concept for dynamically scheduling light-paths based on compute and data location. In acting as alpha-users of new SURFnet BoD/NSI service, the AMC assisted in the debugging of the service. The initial results of this work were presented at a conference (see publications). With an interest in data security, we performed a study "Legal constraints on genetic data processing in European grids" (see publications). In the scope of ER-FLOW a document was produced titled "ethical issues: policy and code of conduct". Elements of this document can be re-used for similar projects (this document can be obtained upon request from the EGI document database here.
Software. Insights in the co-scheduling of compute and data. A Pilot-Data implementation was developed based on DIANE which is capable of running on BiG Grid resources. See the presentation. The code can be obtained here . AMC represented the Life Science community in Staged Rollout of EGI/EMIsoftware (SAGA). This has led to the inclusion of SAGA in the next EMI release. See http://repository.egi.eu/2012/11/20/release-umd-2-3-0/.
Other. The AMC has further developed and operated a workflow-based service that automatically tracks provenance of grid workflow execution. This service, and its communication with BiGGrid resource providers, was supported by this e-biogrid project. The AMC participated and coordinated a task-force in the SCI-BUS project to study new data management functionality for the WS-PGRADE science gateway. See the wiki. A new community has been reached within the AMC, the group of Medical Biochemistry. They are now re-using a workflow from the SHIWA repository for a virtual screening project with Autodock Vina.
Publications. - P*: A Model of Pilot-Abstractions, Andre Luckow, Mark Santcroos, Ole Weidner, Andre Merzky, Pradeep Mantha, Shantenu Jha, 8th IEEE International Conference on e-Science 2012, 2012 - Pilot Abstractions for Compute, Data, and Network, Mark Santcroos, Silvia Delgado Olabarriaga, Daniel S. Katz, Shantenu Jha, NECS Workshop, 8th IEEE International Conference on e-Science 2012, 2012 - Exploring Dynamic Enactment of Scientific Workflows using Pilot-Abstractions, Mark Santcroos, Barbera DC van Schaik, Shayan Shahand, Silvia Delgado Olabarriaga, Andre Luckow, Shantenu Jha ,13th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (accepted), 2013
Magnetic resonance Imagery is a modern technique for recording brain activity. The analysis of this data, both functional and anatomical, will undoubtedly bring new insights in the neural basis of cognitive functioning. Up to now the complexity of analysis has been determined by the available computational power and certain types of approaches have been avoided in typical analytical approaches. In this project we want to evaluate whether is it feasible to use computational heavy approaches to noise reduction, and anatomical and functional connectivity methods, for the normal experiments that are being conducted at the Spinoza Center for NeuroImaging.
Steven Scholte, University of Amsterdam
anticipated results: Matlab module to serve standard fMRI preprocessing tool for all brain and cognition researchers, and a DTI analysis tool for new type of connectivity analysis
Steven Scholte, Sennay Ghebreab, Lourens Waldorp, Caan Matthan
This project will result in an installed environment to enable Pathology Image sharing for translational research between and by the Academic Medical Centers in the Netherlands. Pathology is an important domain in the concept of translational research. Since translational research projects include multi-center studies very often much can be gained by improving the workflow of pathology slides among project participants. Currently, most research facilities have their own stand-alone systems for digital pathology (the term often used when glass slides are captured as whole slide images). Those images are in different file formats (sometimes even proprietary) and there is no infrastructure for collaborating on those images. This project addresses a number of key challenges related to realization of a translational research applications with an IT infrastructure for sharing Pathology images between multiple participating sites with their local Laboratory Information Systems and multi-vendor digital glass slides scanners. The realized infrastructure shall include an open interface to access images for image analysis purposes and an industry standard interface to search for the relevant information based on cross-domain queries from external systems. Besides image sharing within the tEPIS environment it should be integrated with the TraIT context to be able to find digital pathology images from cross-domain queries (e.g. search for all patients that had a particular Gleason score from which MRI images are available and also a tissue sample was stored in the biobank catalogue). This environment will consist of connected Image Management Systems (IMS) per participant, where the connected IMSs will be coupled to the participants specific Laboratory Information System (LIS), when relevant to the pathology reporting system (e.g. U-DPS) and the specific whole slide image scanner. The objective of this environment is to enable sharing of digital pathology images in the context of translational research for purposes like sharing diagnosis, consultation, image analysis (both stand-alone for research purposes and as part of a review workflow), translational (across domains) research and miscellaneous purposes like sharing slides for conferences and publications.
Nikolaos Stathonikus, Universitair Medisch Centrum Utrecht, Pathologie
The Biomarker Boosting project is a collaboration between Radboud University Nijmegen, the Dutch eScience Center, and four Dutch UMCs (VU Amsterdam, Erasmus, Maastricht, Nijmegen). It aims to develop a platform for sharing and joint analysis of imaging data. In this pilot project, the four UMCs contribute a total of 1500 structural MRI scans. An automated hippocampal volume pipeline will be applied to all of these, and the results will be correlated with age, gender and mental disease state. The goal: investigate under what circumstances the pooling of large datasets improves the statistical significance of the computed correlations (biomarkers). To test and debug the pipeline, we request an initial test grant of 20000 core-hours. This test phase should give us a clear estimate of how much additional compute time we will need, and whether the cloud-based solution is the right choice.
Paul Tiesinga, Radboud Universiteit Nijmegen
Rembrandt Bakker, Piter de Boer, e-BioGrid support team
Brain imaging experiments deliver massive amounts of data that need very intensive analysis. In this NWO veni project, full datasets are gathered on which new methods of statistical learning analysis are tested. A Cloud machine on which to run these analysis, which are all based on open-source components, provide a very homogeneous and dependable environment to work on.