A virtual imaging platform for multi-modality medical image simulation.

International audienceThis paper presents the Virtual Imaging Platform (VIP), a platform accessible at http://vip.creatis.insa-lyon.fr to facilitate the sharing of object models and medical image simulators, and to provide access to distributed computing and storage resources. A complete overview is presented, describing the ontologies designed to share models in a common repository, the workflow template used to integrate simulators, and the tools and strategies used to exploit computing and storage resources. Simulation results obtained in four image modalities and with different models show that VIP is versatile and robust enough to support large simulations. The platform currently has 200 registered users who consumed 33 years of CPU time in 2011

misoSR: Medical Image Isotropic Super-Resolution Reconstruction

International audienceIsotropic volumetric acquisition in Magnetic Resonance Imaging (MRI) is often challenging. A large number of factors such as patient or physiological motion, signal-to-noise ratio (SNR), available static magnetic field B 0 and total scanning time limit the acquired resolution. Super-resolution (SR) is a post-processing technique that optimally combines several anisotropic scans into a single isotropic volume that was not-or could not be-acquired in practice. If necessary conditions are met, the resulting isotropic volume offers clear improvements over the initial acquisitions such as reduced partial volume effect, oblique visualization and improved sharp-ness and SNR. This paper details the misoSR implementation of a SR isotropic reconstruction algorithm using the Insight Toolkit Library (ITK) library. It is a generic implementation that reconstructs an isotropic volume from any number of anisotropic volumes acquired from any orientation. The algorithm takes advantage of the inputs header information to handle the different scans properties such as field of view (FOV), resolution parameters and orientation. Step by step details on the implementation are given, parameters are individually detailed, and results are shown on different applications as an example of SR reconstruction. The algorithm is hosted on the Creatis Virtual Imaging Platform (VIP), which allows users to run misoSR without having to install the software on their system

Overview of Medical Data Management Solutions for Research Communities

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Deep Learning Segmentation in 2D echocardiography using the CAMUS dataset : Automatic Assessment of the Anatomical Shape Validity

International audienceWe recently published a deep learning study on the potential of encoder-decoder networks for the segmentation of the 2D CAMUS ultrasound dataset. We propose in this abstract an extension of the evaluation criteria to anatomical assessment, as traditional geometric and clinical metrics in cardiac segmentation do not take into account the anatomical correctness of the predicted shapes. The completed study sheds a new light on the ranking of models

The Virtual Physiological Human ToolKit Rapid response The Virtual Physiological Human TOOLKIT

Respond to this article Subject collections (183 The Virtual Physiological Human (VPH) is a major European e-Science initiative intended to support the development of patient-specific computer models and their application in personalized and predictive healthcare. The VPH Network of Excellence (VPH-NoE) project is tasked with facilitating interaction between the various VPH projects and addressing issues of common concern. A key deliverable is the 'VPH TOOLKIT'-a collection of tools, methodologies and services to support and enable VPH research, integrating and extending existing work across Europe towards greater interoperability and sustainability. Owing to the diverse nature of the field, a single monolithic 'toolkit' is incapable of addressing the needs of the VPH. Rather, the VPH TOOLKIT should be considered more as a 'toolbox' of relevant technologies, interacting around a common set of standards. The latter apply to the information used by tools, including any data and the VPH models themselves, and also to the naming and categorizing of entities and concepts involved. Furthermore, the technologies and methodologies available need to be widely disseminated, and relevant tools and services easily found by researchers. The VPHNoE has thus created an online resource for the VPH community to meet this need. It consists of a database of tools, methods and services for VPH research, with a Web front-end. This has facilities for searching the database, for adding or updating entries, and for providing user feedback on entries. Anyone is welcome to contribute

Challenge on endocardial three-dimensional ultrasound segmentation (CETUS)

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The Virtual Physiological Human ToolKit

The Virtual Physiological Human (VPH) is a major European e-Science initiative intended to support the development of patient-specific computer models and their application in personalized and predictive healthcare. The VPH Network of Excellence (VPH-NoE) project is tasked with facilitating interaction between the various VPH projects and addressing issues of common concern. A key deliverable is the ‘VPH ToolKit’—a collection of tools, methodologies and services to support and enable VPH research, integrating and extending existing work across Europe towards greater interoperability and sustainability. Owing to the diverse nature of the field, a single monolithic ‘toolkit’ is incapable of addressing the needs of the VPH. Rather, the VPH ToolKit should be considered more as a ‘toolbox’ of relevant technologies, interacting around a common set of standards. The latter apply to the information used by tools, including any data and the VPH models themselves, and also to the naming and categorizing of entities and concepts involved. Furthermore, the technologies and methodologies available need to be widely disseminated, and relevant tools and services easily found by researchers. The VPH-NoE has thus created an online resource for the VPH community to meet this need. It consists of a database of tools, methods and services for VPH research, with a Web front-end. This has facilities for searching the database, for adding or updating entries, and for providing user feedback on entries. Anyone is welcome to contribute

Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography

Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the automation of this task has been the subject of intense research. In this paper, we evaluate how far the state-of-the-art encoder-decoder deep convolutional neural network methods can go at assessing 2D echocardiographic images, i.e., segmenting cardiac structures and estimating clinical indices, on a dataset, especially, designed to answer this objective. We, therefore, introduce the cardiac acquisitions for multi-structure ultrasound segmentation dataset, the largest publicly-available and fully-annotated dataset for the purpose of echocardiographic assessment. The dataset contains two and four-chamber acquisitions from 500 patients with reference measurements from one cardiologist on the full dataset and from three cardiologists on a fold of 50 patients. Results show that encoder-decoder-based architectures outperform state-of-the-art non-deep learning methods and faithfully reproduce the expert analysis for the end-diastolic and end-systolic left ventricular volumes, with a mean correlation of 0.95 and an absolute mean error of 9.5 ml. Concerning the ejection fraction of the left ventricle, results are more contrasted with a mean correlation coefficient of 0.80 and an absolute mean error of 5.6%. Although these results are below the inter-observer scores, they remain slightly worse than the intra-observer's ones. Based on this observation, areas for improvement are defined, which open the door for accurate and fully-automatic analysis of 2D echocardiographic images.status: accepte