Nawal Houhou

Fast texture segmentation model based on the shape operator and active contour

We present an approach for unsupervised segmentation of natural and textural images based on active contour, differential geometry and information theoretical concept. More precisely, we propose a new texture descriptor which intrinsically defines the geometry of textural regions using the shape operator borrowed from differential geometry. Then, we use the popular Kullback-Leibler distance to define an active contour model which distinguishes the background and textural objects of interest represented by the probability density functions of our new texture descriptor. We prove the existence of a solution to the proposed segmentation model. Finally, a fast and easy to implement texture segmentation algorithm is introduced to extract meaningful objects. We present promising synthetic and real-world results and compare our algorithm to other state-of-the-art techniques.

Segmentation of Head and Neck Lymph Node Regions for Radiotherapy Planning Using Active Contour-Based Atlas Registration

In this paper, we present the segmentation of the head and neck lymph node regions using a new active contour-based atlas registration model. We propose to segment the lymph node regions without directly including them in the atlas registration process; instead, they are segmented using the dense deformation field computed from the registration of the atlas structures with distinct boundaries. This approach results in robust and accurate segmentation of the lymph node regions even in the presence of significant anatomical variations between the atlas-image and the patients image to be segmented. We also present a quantitative evaluation of lymph node regions segmentation using various statistical as well as geometrical metrics: sensitivity, specificity, dice similarity coefficient and Hausdorff distance. A comparison of the proposed method with two other state of the art methods is presented. The robustness of the proposed method to the atlas selection, in segmenting the lymph node regions, is also evaluated.

Characterization and Clinical Evaluation of CD10+ Stroma Cells in the Breast Cancer Microenvironment

Purpose: There is growing evidence that interaction between stromal and tumor cells is pivotal in breast cancer progression and response to therapy. Based on earlier research suggesting that during breast cancer progression, striking changes occur in CD10+ stromal cells, we aimed to better characterize this cell population and its clinical relevance. Experimental Design: We developed a CD10+ stroma gene expression signature (using HG U133 Plus 2.0) on the basis of the comparison of CD10 cells isolated from tumoral (n = 28) and normal (n = 3) breast tissue. We further characterized the CD10+ cells by coculture experiments of representative breast cancer cell lines with the different CD10+ stromal cell types (fibroblasts, myoepithelial, and mesenchymal stem cells). We then evaluated its clinical relevance in terms of in situ to invasive progression, invasive breast cancer prognosis, and prediction of efficacy of chemotherapy using publicly available data sets. Results: This 12-gene CD10+ stroma signature includes, among others, genes involved in matrix remodeling (MMP11, MMP13, and COL10A1) and genes related to osteoblast differentiation (periostin). The coculture experiments showed that all 3 CD10+ cell types contribute to the CD10+ stroma signature, although mesenchymal stem cells have the highest CD10+ stroma signature score. Of interest, this signature showed an important role in differentiating in situ from invasive breast cancer, in prognosis of the HER2+ subpopulation of breast cancer only, and potentially in nonresponse to chemotherapy for those patients. Conclusions: Our results highlight the importance of CD10+ cells in breast cancer prognosis and efficacy of chemotherapy, particularly within the HER2+ breast cancer disease. Clin Cancer Res; 18(4); 1004–14. ©2012 AACR.

Atlas-based segmentation of medical images locally constrained by level sets

Atlas-based segmentation has become a standard paradigm for exploiting prior knowledge in medical image segmentation. In this paper, we propose a method to exploit both the robustness of global registration techniques and the accuracy of a local registration based on level set tracking. First, the atlas is globally put in correspondence with the patient image by an affine and an intensity-based non rigid registration. Based on this rough initialisation, the level set functions corresponding to particular objects of interest of the deformed atlas are used to segment the corresponding objects in the patient image. We propose a technique to derive a dense deformation field from the motion of these level set functions. This is particularly important when we want to infer the position of invisible structures like the brain sub-thalamic nuclei from the position of visible surrounding structures. This can also be advantageously exploited to register an atlas following a hierarchical approach. Results are shown on 2D synthetic images and 2D real images extracted from brain and prostate MR volumes and neck CT volumes.

Phosphorylation Regulates FOXC2-Mediated Transcription in Lymphatic Endothelial Cells

ABSTRACT One of the key mechanisms linking cell signaling and control of gene expression is reversible phosphorylation of transcription factors. FOXC2 is a forkhead transcription factor that is mutated in the human vascular disease lymphedema-distichiasis and plays an essential role in lymphatic vascular development. However, the mechanisms regulating FOXC2 transcriptional activity are not well understood. We report here that FOXC2 is phosphorylated on eight evolutionarily conserved proline-directed serine/threonine residues. Loss of phosphorylation at these sites triggers substantial changes in the FOXC2 transcriptional program. Through genome-wide location analysis in lymphatic endothelial cells, we demonstrate that the changes are due to selective inhibition of FOXC2 recruitment to chromatin. The extent of the inhibition varied between individual binding sites, suggesting a novel rheostat-like mechanism by which expression of specific genes can be differentially regulated by FOXC2 phosphorylation. Furthermore, unlike the wild-type protein, the phosphorylation-deficient mutant of FOXC2 failed to induce vascular remodeling in vivo. Collectively, our results point to the pivotal role of phosphorylation in the regulation of FOXC2-mediated transcription in lymphatic endothelial cells and underscore the importance of FOXC2 phosphorylation in vascular development.

Semi-supervised Segmentation Based on Non-local Continuous Min-Cut

We propose a semi-supervised image segmentation method that relies on a non-local continuous version of the min-cut algorithm and labels or seeds provided by a user. The segmentation process is performed via energy minimization. The proposed energy is composed of three terms. The first term defines labels or seed points assigned to objects that the user wants to identify and the background. The second term carries out the diffusion of object and background labels and stops the diffusion when the interface between the object and the background is reached. The diffusion process is performed on a graph defined from image intensity patches. The graph of intensity patches is known to better deal with textures because this graph uses semi-local and non-local image information. The last term is the standard TV term that regularizes the geometry of the interface. We introduce an iterative scheme that provides a unique minimizer. Promising results are presented on synthetic textures a nd real-world images.

Semi-Supervised Segmentation of Ultrasound Images Based on Patch Representation and Continuous Min Cut

Ultrasound segmentation is a challenging problem due to the inherent speckle and some artifacts like shadows, attenuation and signal dropout. Existing methods need to include strong priors like shape priors or analytical intensity models to succeed in the segmentation. However, such priors tend to limit these methods to a specific target or imaging settings, and they are not always applicable to pathological cases. This work introduces a semi-supervised segmentation framework for ultrasound imaging that alleviates the limitation of fully automatic segmentation, that is, it is applicable to any kind of target and imaging settings. Our methodology uses a graph of image patches to represent the ultrasound image and user-assisted initialization with labels, which acts as soft priors. The segmentation problem is formulated as a continuous minimum cut problem and solved with an efficient optimization algorithm. We validate our segmentation framework on clinical ultrasound imaging (prostate, fetus, and tumors of the liver and eye). We obtain high similarity agreement with the ground truth provided by medical expert delineations in all applications (94% DICE values in average) and the proposed algorithm performs favorably with the literature.

Shape prior based on statistical map for active contour segmentation

We propose a new method for performing active contour segmentation based on the statistical prior knowledge of the object to detect. From a binary training set of objects, a statistical map describes the possible shapes of the object by computing the probability for each point to belong to the object. This statistical map is treated as a prior distribution and an energy functional is defined such that the object reaches the most probable shape knowing the model. The optimization is done in the level-set framework. Results on both synthetic and medical images are shown.

An efficient segmentation method for ultrasound images based on a semi-supervised approach and patch-based features

Segmenting ultrasound images is a challenging problem where standard unsupervised segmentation methods such as the well-known Chan-Vese method fail. We propose in this paper an efficient segmentation method for this class of images. Our proposed algorithm is based on a semi-supervised approach (user labels) and the use of image patches as data features. We also consider the Pearson distance between patches, which has been shown to be robust w.r.t speckle noise present in ultrasound images. Our results on phantom and clinical data show a very high similarity agreement with the ground truth provided by a medical expert.