Frédéric Cervenansky

A Virtual Imaging Platform for Multi-Modality Medical Image Simulation

This 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 workίow 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.

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.

Plane-Wave Imaging Challenge in Medical Ultrasound

Plane-Wave imaging enables very high frame rates, up to several thousand frames per second. Unfortunately the lack of transmit focusing leads to reduced image quality, both in terms of resolution and contrast. Recently, numerous beamforming techniques have been proposed to compensate for this effect, but comparing the different methods is difficult due to the lack of appropriate tools. PICMUS, the Plane-Wave Imaging Challenge in Medical Ultrasound aims to provide these tools. This paper describes the PICMUS challenge, its motivation, implementation, and metrics.

OntoVIP: An ontology for the annotation of object models used for medical image simulation

This paper describes works carried out in the Virtual Imaging Platform (VIP) project to create a comprehensive conceptualization of object models used in medical image simulation and suitable for the major imaging modalities and simulators. The goal is to create an application ontology that can be used to annotate the models in the VIP platforms model repository, to facilitate their sharing and reuse. Such annotations allow making the anatomical, physiological and pathophysiological content of the object models explicit.

3D Ultrasound real-time monitoring of surgical tools

To assist surgeons during their surgical operations, which involve a tool insertion, a real-time application which is able to localise the surgical tool during its movement is proposed. The position of the needle is estimated with a method based on model fitting using a Random Sample Consensus (RANSAC). Our proposed application has been implemented on the ULTRASONIX RP scanner and it permits 3D volumes acquisition, storage and the tool localization. The developed graphical user interface shows both the section plane containing the tool and the perpendicular plane to it in order to give a view of surrounding tissue. The performed experiments have shown that our application is able to display the correct frame that contains the needle and localize surgical tools in real time and with a good accuracy in water, polyvinyl alcohol cryogel phantom and liver tissue. Refresh rates of displaying the result is less than one second for volumes of size 67 × 128 × 224 voxels. The time required to acquire this volume is about one second with the 4DC7–3/40 3D probe. So our application works at real-time.

The first MICCAI challenge on PET tumor segmentation

Introduction Automatic functional volume segmentation in PET images is a challenge that has been addressed using a large array of methods. A major limitation for the field has been the lack of a benchmark dataset that would allow direct comparison of the results in the various publications. In the present work, we describe a comparison of recent methods on a large dataset following recommendations by the American Association of Physicists in Medicine (AAPM) task group (TG) 211, which was carried out within a MICCAI (Medical Image Computing and Computer Assisted Intervention) challenge. Materials and methods Organization and funding was provided by France Life Imaging (FLI). A dataset of 176 images combining simulated, phantom and clinical images was assembled. A website allowed the participants to register and download training data (n = 19). Challengers then submitted encapsulated pipelines on an online platform that autonomously ran the algorithms on the testing data (n = 157) and evaluated the results. The methods were ranked according to the arithmetic mean of sensitivity and positive predictive value. Results Sixteen teams registered but only four provided manuscripts and pipeline(s) for a total of 10 methods. In addition, results using two thresholds and the Fuzzy Locally Adaptive Bayesian (FLAB) were generated. All competing methods except one performed with median accuracy above 0.8. The method with the highest score was the convolutional neural network‐based segmentation, which significantly outperformed 9 out of 12 of the other methods, but not the improved K‐Means, Gaussian Model Mixture and Fuzzy C‐Means methods. Conclusion The most rigorous comparative study of PET segmentation algorithms to date was carried out using a dataset that is the largest used in such studies so far. The hierarchy amongst the methods in terms of accuracy did not depend strongly on the subset of datasets or the metrics (or combination of metrics). All the methods submitted by the challengers except one demonstrated good performance with median accuracy scores above 0.8. Graphical abstract Figure. No Caption available. HighlightsA comparative study of 13 PET segmentation methods was carried out within a MICCAI challenge.The evaluation methodology followed guidelines recently published by the AAPM task group 211.Accuracy was evaluated on a testing dataset of 157 simulated, phantom and clinical PET images.Most of the advanced algorithms performed well and significantly better than thresholds.A method based on convolutional neural networks won the challenge.

Creatools: a framework to develop medical image processing software: application to simulate pipeline stent deployment in intracranial vessels with aneurysms

The paper presents a collaborative project that offers stand-alone software applications for end-users and a complete open-source platform to rapidly develop/prototype medical image processing work-flows with sophisticated visualization and user interactions. It builds on top of a flexible cross-platform framework (Linux, Windows and MacOS) developed in C++, which guarantees an easy connection of heterogeneous C++ modules and provides the user with libraries of high-level components to construct graphical user interfaces (GUI) including input/output (file management), display, interaction, data processing, etc. In this article, we illustrate the usefulness of this framework through a research project dealing with the study of thrombosis in intra-cranial aneurysms. Algorithms developed by the researchers, such as image segmentation, stent model generation, its interactive virtual deployment in the segmented vessels, as well as the generation of meshes necessary to simulate the blood flow through thus stented vessels, have been implemented in a user-friendly GUI with 3D visualization and interaction.

OntoVIP: An Ontology for the Annotation of Object Models Used for Medical Image Simulation

This paper describes works carried out in the Virtual Imaging Platform (VIP) project to create a comprehensive conceptualization of object models used in medical image simulation and suitable for the major imaging modalities and simulators. The goal is to create an application ontology that can be used to annotate the models in the VIP platforms model repository, to facilitate their sharing and reuse. Such annotations allow making the anatomical, physiological and pathophysiological content of the object models explicit.

Overview of Medical Data Management Solutions for Research Communities

Medical imaging research deals with large, heterogeneous and fragmented amounts of medical images. The need for secure, federated and functional medical image databases is very strong within these research communities. This paper provides an overview of the different projects concerned with building medical image databases for medical imaging research. It also discusses the characteristics and requirements of this community and tries to determine to what extent existing solutions can answer these specific requirements.

Robustness of spatio-temporal regularization in perfusion MRI deconvolution: An application to acute ischemic stroke

The robustness of a recently introduced globally convergent deconvolution algorithm with temporal and edge‐preserving spatial regularization for the deconvolution of dynamic susceptibility contrast perfusion magnetic resonance imaging is assessed in the context of ischemic stroke.