3D Convolutional Neural Networks for Tumor Segmentation using Long-range 2D Context

We present an efficient deep learning approach for the challenging task of tumor segmentation in multisequence MR images. In recent years, Convolutional Neural Networks (CNN) have achieved state-of-the-art performances in a large variety of recognition tasks in medical imaging. Because of the considerable computational cost of CNNs, large volumes such as MRI are typically processed by subvolumes, for instance slices (axial, coronal, sagittal) or small 3D patches. In this paper we introduce a CNN-based model which efficiently combines the advantages of the short-range 3D context and the long-range 2D context. To overcome the limitations of specific choices of neural network architectures, we also propose to merge outputs of several cascaded 2D-3D models by a voxelwise voting strategy. Furthermore, we propose a network architecture in which the different MR sequences are processed by separate subnetworks in order to be more robust to the problem of missing MR sequences. Finally, a simple and efficient algorithm for training large CNN models is introduced. We evaluate our method on the public benchmark of the BRATS 2017 challenge on the task of multiclass segmentation of malignant brain tumors. Our method achieves good performances and produces accurate segmentations with median Dice scores of 0.918 (whole tumor), 0.883 (tumor core) and 0.854 (enhancing core). Our approach can be naturally applied to various tasks involving segmentation of lesions or organs.Comment: Submitted to the journal Computerized Medical Imaging and Graphic

Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow

We propose a method to classify cardiac pathology based on a novel approach to extract image derived features to characterize the shape and motion of the heart. An original semi-supervised learning procedure, which makes efficient use of a large amount of non-segmented images and a small amount of images segmented manually by experts, is developed to generate pixel-wise apparent flow between two time points of a 2D+t cine MRI image sequence. Combining the apparent flow maps and cardiac segmentation masks, we obtain a local apparent flow corresponding to the 2D motion of myocardium and ventricular cavities. This leads to the generation of time series of the radius and thickness of myocardial segments to represent cardiac motion. These time series of motion features are reliable and explainable characteristics of pathological cardiac motion. Furthermore, they are combined with shape-related features to classify cardiac pathologies. Using only nine feature values as input, we propose an explainable, simple and flexible model for pathology classification. On ACDC training set and testing set, the model achieves 95% and 94% respectively as classification accuracy. Its performance is hence comparable to that of the state-of-the-art. Comparison with various other models is performed to outline some advantages of our model

A model of brain morphological changes related to aging and Alzheimer's disease from cross-sectional assessments

In this study we propose a deformation-based framework to jointly model the influence of aging and Alzheimer's disease (AD) on the brain morphological evolution. Our approach combines a spatio-temporal description of both processes into a generative model. A reference morphology is deformed along specific trajectories to match subject specific morphologies. It is used to define two imaging progression markers: 1) a morphological age and 2) a disease score. These markers can be computed locally in any brain region. The approach is evaluated on brain structural magnetic resonance images (MRI) from the ADNI database. The generative model is first estimated on a control population, then, for each subject, the markers are computed for each acquisition. The longitudinal evolution of these markers is then studied in relation with the clinical diagnosis of the subjects and used to generate possible morphological evolution. In the model, the morphological changes associated with normal aging are mainly found around the ventricles, while the Alzheimer's disease specific changes are more located in the temporal lobe and the hippocampal area. The statistical analysis of these markers highlights differences between clinical conditions even though the inter-subject variability is quiet high. In this context, the model can be used to generate plausible morphological trajectories associated with the disease. Our method gives two interpretable scalar imaging biomarkers assessing the effects of aging and disease on brain morphology at the individual and population level. These markers confirm an acceleration of apparent aging for Alzheimer's subjects and can help discriminate clinical conditions even in prodromal stages. More generally, the joint modeling of normal and pathological evolutions shows promising results to describe age-related brain diseases over long time scales.Comment: NeuroImage, Elsevier, In pres

A perception and manipulation system for collecting rock samples

An important part of a planetary exploration mission is to collect and analyze surface samples. As part of the Carnegie Mellon University Ambler Project, researchers are investigating techniques for collecting samples using a robot arm and a range sensor. The aim of this work is to make the sample collection operation fully autonomous. Described here are the components of the experimental system, including a perception module that extracts objects of interest from range images and produces models of their shapes, and a manipulation module that enables the system to pick up the objects identified by the perception module. The system was tested on a small testbed using natural terrain

Simulation : Le coeur numérique par Hervé Delingette et Miguel Fernandez. De l'ordinateur à la clinique, entretien avec Jérôme Garot, propos recueillis par Dominique Chouchan. Améliorer les performances des implants cardiaques par Alain Ripart

National audienceUn modèle numérique du coeur et des fonctions cardiaques, c'est presque pour demain. Médecins et informaticiens s'associent pour s'attaquer aux maladies cardiovasculaires, la première cause de mortalité dans le monde

Deliverable D10.4.1

This deliverable describes the final status of Task 10.4 of Workpackage 10 of the euHeart project. The aim of this task is to develop a prototype of an endovascular simulator of cardiac radiofrequency ablation. More precisely, its purpose is to simulate the patient-specific catheter navigation and radiofre- quency ablation of ventricular tachycardia. Since deliverable 10.4.1, work on the simulator prototype has focused on the development of a user interface and the integration of two software compo- nents : endovascular simulation and electrophysiology simulation. The first component aims at modeling the deformation of catheters and guidewires inside vessels and to generate a realistic visualization of the vis- ible X-ray images. The second component is focused on the simulation of electrophysiology. We have chosen the Mitchell-Schaeffer phenomenological model to represent the evolution of action potential on the myocardium. The integration of those 2 software components is difficult because they should both run simultaneously in real-time. To this end, we have developed a multi-thread framework allowing to parallelize the computation of the catheter deformation and the cardiac electrophysiology while sharing a minimum num- ber of information. We have also developed a user interface that can display X-ray images, 3D view of the heart and simulated electro-physiology signals measured at the tip of the catheter. An example of simulation is provided starting from the endovascular navi- gation from the veina cava and finishing with the radiofrequency ablation of endocardial tissue inside the right ventricle

Deliverable D10.4.2

This deliverable describes the final status of Task 10.4 of Workpackage 10 of the euHeart project. The aim of this task is to develop a prototype of an endovascular simulator of cardiac radiofrequency ablation. More precisely, its purpose is to simulate the patient-specific catheter navigation and radiofre- quency ablation of ventricular tachycardia. Since deliverable 10.4.1, work on the simulator prototype has focused on the development of a user interface and the integration of two software compo- nents : endovascular simulation and electrophysiology simulation. The first component aims at modeling the deformation of catheters and guidewires inside vessels and to generate a realistic visualization of the vis- ible X-ray images. The second component is focused on the simulation of electrophysiology. We have chosen the Mitchell-Schaeffer phenomenological model to represent the evolution of action potential on the myocardium. The integration of those 2 software components is difficult because they should both run simultaneously in real-time. To this end, we have developed a multi-thread framework allowing to parallelize the computation of the catheter deformation and the cardiac electrophysiology while sharing a minimum num- ber of information. We have also developed a user interface that can display X-ray images, 3D view of the heart and simulated electro-physiology signals measured at the tip of the catheter. An example of simulation is provided starting from the endovascular navi- gation from the veina cava and finishing with the radiofrequency ablation of endocardial tissue inside the right ventricle

Haptic Rendering of Hyperelastic Models with Friction

International audience— This paper presents an original method for inter-actions' haptic rendering when treating hyperelastic materials. Such simulations are known to be difficult due to the non-linear behavior of hyperelastic bodies; furthermore, haptic constraints enjoin contact forces to be refreshed at least at 1000 updates per second. To enforce the stability of simulations of generic objects of any range of stiffness, this method relies on implicit time integration. Soft tissues dynamics is simulated in real time (20 to 100 Hz) using the Multiplicative Jacobian Energy Decomposition (MJED) method. An asynchronous preconditioner, updated at low rates (1 to 10 Hz), is used to obtain a close approximation of the mechanical coupling of interactions. Finally, the contact problem is linearized and, using a specific-loop, it is updated at typical haptic rates (around 1000 Hz) allowing this way new simulations of prompt stiff-contacts and providing a continuous haptic feedback as well

Towards an Interactive Electromechanical Model of the Heart

International audienceIn this work, we develop an interactive framework for rehearsal and training in the context of cardiac catheter ablation, and for planning in the context of Cardiac Resynchronization Therapy (CRT). To this end, an interactive and real-time electrophysiology model of the heart is developed to fit patient-specific data. The proposed interactive framework relies on two main contributions. An efficient implementation of cardiac electrophysiology is first proposed using latest GPU computing techniques. Second, a mechanical simulation is then coupled to the electrophysiological signals to produce realistic motion of the heart. We demonstrate that pathological mechanical and electrophysiological behaviour can be simulated