Biomedical subjects

J Montagnat

Publications and source records attributed to J Montagnat.

5 recordsLinked to original sources

PubMed2005

Grid-enabling medical image analysis.

Grids have emerged as a promising technology to handle the data and compute intensive requirements of many application areas. Digital medical image processing is a promising application area for grids. Given the volume of data, the sensitivity of medical information, and the joint complexity of medical datasets and computations expected in clinical practice, the challenge is to fill the gap between the grid middleware and the requirements of clinical applications. The research project AGIR (Grid Analysis of Radiological Data) presented in this paper addresses this challenge through a combined approach: on one hand, leveraging the grid middleware through core grid medical services which target the requirements of medical data processing applications; on the other hand, grid-enabling a panel of applications ranging from algorithmic research to clinical applications.

Computer Communication Networks↗

PubMed2005

The Healthgrid White Paper.

Over the last four years, a community of researchers working on Grid and High Performance Computing technologies started discussing the barriers and opportunities that grid technologies must face and exploit for the development of health-related applications. This interest lead to the first Healthgrid conference, held in Lyon, France, on January 16th-17th, 2003, with the focus of creating increased awareness about the possibilities and advantages linked to the deployment of grid technologies in health, ultimately targeting the creation of a European/international grid infrastructure for health. The topics of this conference converged with the position of the eHealth division of the European Commission, whose mandate from the Lisbon Meeting was "To develop an intelligent environment that enables ubiquitous management of citizens' health status, and to assist health professionals in coping with some major challenges, risk management and the integration into clinical practice of advances in health knowledge." In this context "Health" involves not only clinical procedures but covers the whole range of information from molecular level (genetic and proteomic information) over cells and tissues, to the individual and finally the population level (social healthcare). Grid technology offers the opportunity to create a common working backbone for all different members of this large "health family" and will hopefully lead to an increased awareness and interoperability among disciplines. The first HealthGrid conference led to the creation of the Healthgrid association, a non-profit research association legally incorporated in France but formed from the broad community of European researchers and institutions sharing expertise in health grids. After the second Healthgrid conference, held in Clermont-Ferrand on January 29th-30th, 2004, the need for a "white paper" on the current status and prospective of health grids was raised. Over fifty experts from different areas of grid technologies, eHealth applications and the medical world were invited to contribute to the preparation of this document.

Computer Communication Networks↗

PubMed2005

Partitioning medical image databases for content-based queries on a Grid.

OBJECTIVES: In this paper we study the impact of executing a medical image database query application on the grid. For lowering the total computation time, the image database is partitioned into subsets to be processed on different grid nodes. METHODS: A theoretical model of the application complexity and estimates of the grid execution overhead are used to efficiently partition the database. RESULTS: We show results demonstrating that smart partitioning of the database can lead to significant improvements in terms of total computation time. CONCLUSIONS: Grids are promising for content-based image retrieval in medical databases.

Database Management Systems↗

PubMed2003

DataGrid, prototype of a biomedical grid.

BACKGROUND: The availability of large amounts of data in heterogeneous formats and the rapid progress in fields such as computer based drug design, medical imaging and medical simulations have lead to a growing demand for large computational power and easy accessibility to heterogeneous data sources. OBJECTIVES: The goal is to address these needs by deploying computing grids. Grids provide both large scale and distributed storage facilities and an increased computing power. Moreover, Grids are a promising tool to foster the synergy between bioinformatics and computerised medical imaging. METHODS: A first biomedical grid is being deployed within the framework of the DataGrid IST project (http://www.edg.org). The goal of the project is to provide a novel environment to support globally distributed scientific exploration involving up to multi-Perabyte datasets. RESULTS AND CONCLUSIONS: The first biomedical applications deployed inside the project demonstrate the relevance of the grid paradigm for genomics and medical image processing. They also highlight the specific requirements of the biomedical community.

Biomedical Research↗

PubMed2001

Fully automatic anatomical, pathological, and functional segmentation from CT scans for hepatic surgery.

OBJECTIVE: To improve the planning of hepatic surgery, we have developed a fully automatic anatomical, pathological, and functional segmentation of the liver derived from a spiral CT scan. MATERIALS AND METHODS: From a 2 mm-thick enhanced spiral CT scan, the first stage automatically delineates skin, bones, lungs, kidneys, and spleen by combining the use of thresholding, mathematical morphology, and distance maps. Next, a reference 3D model is immersed in the image and automatically deformed to the liver contours. Then an automatic Gaussian fitting on the imaging histogram estimates the intensities of parenchyma, vessels, and lesions. This first result is next improved through an original topological and geometrical analysis, providing an automatic delineation of lesions and veins. Finally, a topological and geometrical analysis based on medical knowledge provides hepatic functional information that is invisible in medical imaging: portal vein labeling and hepatic anatomical segmentation according to the Couinaud classification. RESULTS: Clinical validation performed on more than 30 patients shows that delineation of anatomical structures by this method is often more sensitive and more specific than manual delineation by a radiologist. CONCLUSION: This study describes the methodology used to create the automatic segmentation of the liver with delineation of important anatomical, pathological, and functional structures from a routine CT scan. Using the methods proposed in this study, we have confirmed the accuracy and utility of the creation of a 3D liver model compared with the conventional reading of the CT scan by a radiologist. This work may allow improved preoperative planning of hepatic surgery by more precisely delineating liver pathology and its relationship to normal hepatic structures. In the future, this data may be integrated with computer-assisted surgery and thus represents a first step towards the development of an augmented-reality surgical system.

Humans↗