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

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

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

Analyzing 3D images of the brain

International audienceDuring the past 5 years, there has been a considerable effort of research in automating the analysis and fusion of multidimensional images, yielding important theoretical and practical new results. These results could now be useful to brain research. Along these lines, and focusing on 3D images of the brain obtained with CT, MRI, SPECT, and PET techniques, I will present a number of methods useful to produce quantitative measurements necessary for an objective analysis of 3D images of the brain. Such methods include segmentation, shape analysis, rigid and elastic registration, fusion of multimodal images, analysis of temporal sequences (4D data), modeling, and matching of digital atlases. A large number of references on these topics can be found in Ayache (1995a,b), and specific examples from our research group in Malandain et al. (1994), Subsol et al. (1995), and Thirion (1995). The following text describes the research tracks to be followed in order to optimize the future exploitation of 3D images of the brain in neuroscience

L'analyse automatique des images médicales : Etat de l'art et perspectives

Les logiciels de traitement et d'analyse des images médicales peuvent apporter au médecin une aide considérable pour le diagnostic et la pratique thérapeutique. Cet article introduit les potentialités offertes par ces nouveaux outils, et présente court un état de l'art des activités de recherche dans ce domaine

Foreword

International audienc

Medical Imaging in the Age of Artificial Intelligence

International audienc

Computer Vision Applied to 3D Medical Images: Results, Trends and Future Challenges

Available as INRIA Research Report 2050.International audienceRésumé disponible dans le fichier attach

Research Tracks in Computer Vision Applied to 3D Medical Images

Abstract This short l~per is mainly the introduction oJ the a~ailable INRIA Research Report No. 2050 (55 page, ) published by the author in Sept. 1993 [1]. The automated analysis of 3D medical images can improve significantly both diagnosis and therapy. This automation raises a number of new fascinating research problems in the fields of computer vision and robotics. In this paper, I propose a list of such problems after a review of the current major 3D imaging modalities, and a description of the related medical needs. I then present some of past and current work done in the research group EPIDAURE ] at INRIA, on the following topics: segmentation of 3D images, 3D shape modeling, 3D rigid and nonrigid registration, 3D motion analysis and 3D simulation of therapy. I also'try to suggest a number of promising research tracks and future challenges