Adélaïde Albouy-Kissi

Variational myocardial tracking from CINE-MRI with non-linear regularization

We present a new motion estimation approach for cardiac Magnetic Resonance Imaging (MRI) data from a variational framework. The improved performance of variational approach has been achieved by designing a new regularization term that properly handles motion discontinuities. This approach was applied to both synthetic and real data. The quantitative evaluation revealed the superior performance of the proposed method against reference approaches.

Variational myocardial tracking from cine-MRI with non-linear regularization: validation of radial displacements vs. tagged-MRI

We present a new motion estimation approach for cardiac Magnetic Resonance Imaging (Cine-MRI) data from variational framework. The improved performance of this variational approach has been achieved by designing a new regularization term that properly handles motion discontinuities. This approach was applied to both synthetic and real data. The quantitative evaluation revealed that the results of proposed method on cine-MRI correlates with the results given by inTag, reference approach on tagged-MRI.

Automatic scale-independent morphology-based quantification of liver fibrosis

The pathologists have an expert knowledge of the classification of fibrosis. However, the differentiation of intermediate grades (ex: F2-F3) may cause significant inter-expert variability. A quantitative morphological marker is presented in this paper, introducing a local-based image analysis on human liver tissue slides. Having defined hotspots in slides, the liver collagen is segmented with a color deconvolution technique. After removing the regions of interstitial fibrosis, the fractal dimension of the fibrosis regions is computed by using the boxcounting algorithm. As a result, a quantitative index provides information about the grade of the fibrosis regions and thus about the tissue damage. The index does not take account of the pathological status of the patient but it allows to discriminate accurately and objectively the intermediate grades for which the expert evaluation is partially based on the fibrosis development. This method was used on twelve human liver biopsies (from six different patients) using constant conditions of preparation, acquisition (same image resolution, magnification x20) and box-counting parameters. The liver tissue slides were labeled by a pathologist using METAVIR scores. A reasonably good correlation is observed between the METAVIR scores and the proposed morphological index (p-value < 0:001). Furthermore, the method is reproducible and scale independent which is appropriate for biological high resolution images. Nevertheless, further work is needed to define reference values for this index in such a way that METAVIR subdomains will be well delimited.

A subset-search and ranking based feature-selection for histology image classification using global and local quantification

Biopsy remains the gold standard for the diagnosis of chronic liver diseases. However, the variability in the diagnostic between readers leads to define a method to objectively describe histologic tissue. A complete framework has been implemented to analyze images of any tissue. Based on subset selection and feature ranking approaches, a feature selection computes the most relevant subset of descriptors in terms of classification from a wide initial list of descriptors. In comparison with equivalent methods, this implementation can find lists of descriptors which are significantly shorter for an equivalent accuracy. Furthermore, it enables the classification of slides using combinations of global and local measurements. The results have pointed that it could reach an accuracy of 90.5% (ROC-AUC=81.1%) in a human liver fibrosis grading approach by selecting 3 of the 457 global and local descriptors. The feature ranking approach gave less accurate subsets than the subset selection.

Perfusion quantification of contrast-enhanced ultrasound images based on coherence enhancing diffusion and competitive clustering

The aim of this paper is to introduce a novel approach to quantify liver arterial perfusion. In this study, the contrast-enhanced ultrasound modality is used to investigate liver diseases. The results show the pertinence of this approach in liver carcinoma. The method is based on perfusion estimation, in a regional basis and characterized by a fully-automated tracking of lesion in images sequences based on image statistics. This approach proceeds in two steps in a new framework, a first PDE algorithm and a second segmentation step by competitive clustering.

A new iterative method for liver segmentation from perfusion CT scans

Liver cancer is the third most common cancer in the world, and the majority of patients with liver cancer will die within one year as a result of the cancer. Liver segmentation in the abdominal area is critical for diagnosis of tumor and for surgical procedures. Moreover, it is a challenging task as liver tissue has to be separated from adjacent organs and substantially the heart. In this paper we present a novel liver segmentation iterative method based on Fuzzy C-means (FCM) coupled with a fast marching segmentation and mutual information. A prerequisite for this method is the determination of slice correspondences between ground truth that is, a few images segmented by an expert, and images that contain liver and heart at the same time.

Fast CEUS image segmentation based on self organizing maps

Contrast-enhanced ultrasound (CEUS) has recently become an important technology for lesion detection and characterization. CEUS is used to investigate the perfusion kinetics in tissue over time, which relates to tissue vascularization. In this paper, we present an interactive segmentation method based on the neural networks, which enables to segment malignant tissue over CEUS sequences. We use Self-Organizing-Maps (SOM), an unsupervised neural network, to project high dimensional data to low dimensional space, named a map of neurons. The algorithm gathers the observations in clusters, respecting the topology of the observations space. This means that a notion of neighborhood between classes is defined. Adjacent observations in variables space belong to the same class or related classes after classification. Thanks to this neighborhood conservation property and associated with suitable feature extraction, this map provides user friendly segmentation tool. It will assist the expert in tumor segmentation with fast and easy intervention. We implement SOM on a Graphics Processing Unit (GPU) to accelerate treatment. This allows a greater number of iterations and the learning process to converge more precisely. We get a better quality of learning so a better classification. Our approach allows us to identify and delineate lesions accurately. Our results show that this method improves markedly the recognition of liver lesions and opens the way for future precise quantification of contrast enhancement.