Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure

We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning, …), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores

Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the "Automatic Cardiac Diagnosis Challenge" dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies. In the wake of the 2017 MICCAI-ACDC challenge, we report results from deep learning methods provided by nine research groups for the segmentation task and four groups for the classification task. Results show that the best methods faithfully reproduce the expert analysis, leading to a mean value of 0.97 correlation score for the automatic extraction of clinical indices and an accuracy of 0.96 for automatic diagnosis. These results clearly open the door to highly accurate and fully automatic analysis of cardiac CMRI. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for new submissions

Slow pressure waves in the cranial enclosure

International audienc

Plane-wave imaging challenge in medical ultrasound

International audienc

Modifications of spontaneous cerebral blood flow oscillations during cardiopulmonary bypass

International audienc

CreaTools: A development framework for medical image processing software ; an application to segmentation, anomaly detection and quantification for coronary arteries

Présentation disponible sur le site de la conférence: http://eubias2013.irbbarcelona.org/meeting-reportInternational audienceCreaTools provides stand-alone applications for end users and a cross-platform framework that helps researchers in the validation of their medical image processing algorithms. As an open source platform initiated at CREATIS, it provides tools to quickly prototype an interface, choose a sophisticated visualization, add interactivity with the image and apply processing(s) to be tested. It has been applied to cardio-vascular studies, the analysis of maxillofacial bones, the segmentation of corals and the quantification of cerebral perfusion, visceral adipose tissue, pulmonary ventilation, etc. The basic elements, widgets (e.g. DICOM browser) or algorithms, are capitalized in black boxes, the kernel of CreaTools being BBTK (Black Box Tool Kit). These boxes are interconnected via heterogeneous C++ modules, in a pipeline mode, using a script language or a graphical interface. The boxes are based on the widely used open-source third-party libraries, ITK, VTK, wxWidgets and Qt. Recently, a new tool has been developed to help new users, CreaDevManager. CMake use is now transparent and a graphical interface is provided to guide the developer. CreaTools is not only suitable for quick prototyping but also can be used to design final applications, the final user being a researcher or a medical doctor. For example, CreaCoro is a CreaTools interface aiming at the visualization of anomalies in coronary arteries. Based on an input axis and an image, it extracts the vessel, produces a linear view of it (CPR), allowing to see the lumen slice by slice. Several segmentation, anomaly detection and quantification algorithms have been tested thanks to this interface. Their results can be visualized by superposition on the input image. This gives a feedback on the algorithm accuracy towards the detection of the anomaly, its quantification and the lumen segmentation, by comparison on the 3D, axial and CPR views

CreaTools: applications and development framework for medical image-processing software

International audienc

CreaTools: a framework to develop medical image processing software. Application to simulate pipeline stent deployment in intracranial vessels with aneurysms

International audienc

Global Initiative for Sentinel e-Health Network on Grid (GINSENG): Medical Data Integration and Semantic Developments for Epidemiology

International audienceThe implementation of a grid network to support large-scale epidemiology analysis (based on distributed medical data sources) and medical data sharing require medical data integration and semantic alignment. In this paper, we present the GINSENG (Global Initiative for Sentinel e-Health Network on Grid) network that federates existing Electronic Health Records through a rich metamodel (FedEHR), a semantic data model (SemEHR) and distributed query toolkits. A query interface based on the VIP platform, and available through the e-ginseng.org web portal helps medical end-users in the design of epidemiological studies and the retrieval of relevant medical data sets

Design of an ontology for the annotation of object models used for medical image simulation

National audienc