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Surface-based approaches have proven particularly relevant and reliable to study cortical functional magnetic resonance imaging (fMRI) data. However projecting fMRI volumes onto the cortical surface remains a challenging problem. Very few methods have been proposed to solve it and most of them rely on a simple interpolation. We propose here an original surface-based method based on a model representing...
We propose an automated framework for lung nodule segmentation from pulmonary CT scan using graph cut with a deep learned prior. The segmentation problem is formulated as a hybrid cost function minimization task, which combines a domain specific data term with a deep learned probability map. The proposed segmentation framework embodies the robustness of deep learning in object localization, while...
Registration of diffusion weighted datasets remains a challenging task in the process of quantifying diffusion indexes. Respiratory and cardiac motion, as well as echo-planar characteristic geometric distortions, may greatly limit accuracy on parameter estimation, specially for the liver. This work proposes a methodology for the non-rigid registration of multiparametric abdominal diffusion weighted...
This paper presents a novel longitudinal framework for clinical score prediction in Alzheimer's disease (AD) diagnosis. In contrast to the previous approaches that use the data collected at a single time point only for the clinical score prediction, we propose to exploit the imaging data of multiple time points. Furthermore, a spatial-temporal group sparse method is proposed for robust feature selection...
Retinal Neovascularization (NV) is a critical stage of Diabetic Retinopathy (DR) and its detection is important to prevent blindness. Existing fully supervised frameworks typically take a patch-based approach and report good results only on limited number of images due to sparsity of annotated data. We propose a patch-based semi-supervised framework which paves the way for including unlabeled data...
Mandible bone segmentation from computed tomography (CT) scans is challenging due to mandible's structural irregularities, complex shape patterns, and lack of contrast in joints. Furthermore, connections of teeth to mandible and mandible to remaining parts of the skull make it extremely difficult to identify mandible boundary automatically. This study addresses these challenges by proposing a novel...
Sub-concussive asymptomatic head impacts during contact sports may develop potential neurological changes and may have accumulative effect through repetitive occurrences in contact sports like American football. The effects of sub-concussive head impacts on the functional connectivity of the brain are still unclear with no conclusive results yet presented. Although various studies have been performed...
In this work we set several mathematical tools to study the role of Dynamin and Endophilin in the Clathrin-mediated endocytosis process. Their different dynamics and co-localizations are observed by using TIRF microscopy. We define in particular a novel tracking method in order to track the Clathrin-coated pits and quantify their co-localization with the proteins involved in the process. Thereafter,...
In this paper, we address the problem of automated pose classification and segmentation of the left ventricle (LV) in 2D echocardiographic images. For this purpose, we compare two complementary approaches. The first one is based on engineering ad-hoc features according to the traditional machine learning paradigm. Namely, we extract phase features to build an unsupervised LV pose estimator, as well...
The detection of cells and nuclei is a crucial step for the automatic analysis of digital pathology slides and as such for the quantification of the phenotypic information contained in tissue sections. This task is however challenging because of high variability in size, shape and textural appearance of the objects to be detected and of the high variability of tissue appearance. In this work, we propose...
A variety of parametric models based on the Apparent Diffusion Coefficient (ADC) have been proposed to describe signal decay in the interpretation of diffusion weighted Magnetic Resonance Imaging data. In this work, we investigate the robustness of several of these models, including the exponential model, bi-exponential model and the recently proposed gamma distribution model, using a Crámer-Rao lower...
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