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

Selective block of Ca2+-dependent K+ current in crayfish neuromuscular system and chromaffin cells by sea anemone Bunodosoma cangicum venom

The effects of the nematocyst venom of the sea anemone Bunodosoma cangicum on depolarization-activated currents were studied in opener crayfish muscle fibers and in cultured bovine chromaffin cells. The venom selectively and reversibly blocked the Ca2+-dependent K+ current (I(K(Ca))) present in crayfish muscle in a dose-dependent manner without affecting voltage-gated Ca2+ or K+ currents. Furthermore, the venom also reduced I(K(Ca)) in chromaffin cells, without modifying voltage-gated Na+, Ca2+, or K+ currents. Synaptic transmission in crayfish muscle was also affected by the venom. Repetitive excitatory and inhibitory postsynaptic currents (each associated with a presynaptic action potential) were evoked by each nerve stimulus, suggesting that presynaptic I(K(Ca)) may control the electrical activity of excitatory and inhibitory presynaptic fibers. We conclude that B. cangicum venom includes a toxin that selectively and reversibly blocks Ca2+-dependent K+ currents in crayfish muscle and in bovine chromaffin cells, and modifies excitatory and inhibitory synaptic transmission, probably abolishing a similar conductance at the presynaptic fibers.Peer Reviewe

Modifications of spontaneous cerebral blood flow oscillations during cardiopulmonary bypass

International audienc

Suite logicielle pour l’imagerie de perfusion dans l’étude de l’accident vasculaire cérébral

International audienceL’accident vasculaire cérébral (AVC), est un problème majeur de santé public qui représente la 3ième cause de mortalité dans les pays industrialisés. Il est nécessaire d’améliorer l’identification des patients éligibles à différentes thérapies et à l’évaluation du ratio risquebénéfice. Dans ce contexte, l’imagerie (3D+temps) par résonance magnétique de perfusion, pendant laquelle on enregistre en chaque voxel la réponse des tissus au passage d’un agent de contraste de susceptibilité magnétique, permet l’évaluation sur des bases hémodynamiques des tissus à risques. Toutefois, le pipeline associant à la fois la gestion et le traitement de ces données sur des cohortes de patients est complexe et nécessite encore des développements, comme l’illustre cette communication qui présente des outils logiciels d’introduction récente dans ce contexte (1-8).1. Frindel, C. et al . Stroke, 46(4), 976-981, 2015.2. Frindel, C. et al. Journées Démonstrateurs club EEA. 1-8 2013.3. Frindel, C. et al. Medical Image Analysis, 18(1), 144-160 2014.4. Giacalone. M. et al. GRETSI, 1-4, 2015.5. Giacalone, M. et al. IEEE IWSSIP, 1.-4, 20166. Giacalone M. et al EUSIPCO, 1.-8, 20167. Giacalone, M. et al Update in Stoke. Book chapter (in press 2017)8. Giacalone, M. et al, Magnetic Resonance in Medicine (in press)

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