Contributions à la fusion de segmentations et à l’interprétation sémantique d’images

Cette thèse est consacrée à l’étude de deux problèmes complémentaires, soit la fusion de segmentation d’images et l’interprétation sémantique d’images. En effet, dans un premier temps, nous proposons un ensemble d’outils algorithmiques permettant d’améliorer le résultat final de l’opération de la fusion. La segmentation d’images est une étape de prétraitement fréquente visant à simplifier la représentation d’une image par un ensemble de régions significatives et spatialement cohérentes (également connu sous le nom de « segments » ou « superpixels ») possédant des attributs similaires (tels que des parties cohérentes des objets ou de l’arrière-plan). À cette fin, nous proposons une nouvelle méthode de fusion de segmentation au sens du critère de l’Erreur de la Cohérence Globale (GCE), une métrique de perception intéressante qui considère la nature multi-échelle de toute segmentation de l’image en évaluant dans quelle mesure une carte de segmentation peut constituer un raffinement d’une autre segmentation. Dans un deuxième temps, nous présentons deux nouvelles approches pour la fusion des segmentations au sens de plusieurs critères en nous basant sur un concept très important de l’optimisation combinatoire, soit l’optimisation multi-objectif. En effet, cette méthode de résolution qui cherche à optimiser plusieurs objectifs concurremment a rencontré un vif succès dans divers domaines. Dans un troisième temps, afin de mieux comprendre automatiquement les différentes classes d’une image segmentée, nous proposons une approche nouvelle et robuste basée sur un modèle à base d’énergie qui permet d’inférer les classes les plus probables en utilisant un ensemble de segmentations proches (au sens d’un certain critère) issues d’une base d’apprentissage (avec des classes pré-interprétées) et une série de termes (d’énergie) de vraisemblance sémantique.This thesis is dedicated to study two complementary problems, namely the fusion of image segmentation and the semantic interpretation of images. Indeed, at first we propose a set of algorithmic tools to improve the final result of the operation of the fusion. Image segmentation is a common preprocessing step which aims to simplify the image representation into significant and spatially coherent regions (also known as segments or super-pixels) with similar attributes (such as coherent parts of objects or the background). To this end, we propose a new fusion method of segmentation in the sense of the Global consistency error (GCE) criterion. GCE is an interesting metric of perception that takes into account the multiscale nature of any segmentations of the image while measuring the extent to which one segmentation map can be viewed as a refinement of another segmentation. Secondly, we present two new approaches for merging multiple segmentations within the framework of multiple criteria based on a very important concept of combinatorial optimization ; the multi-objective optimization. Indeed, this method of resolution which aims to optimize several objectives concurrently has met with great success in many other fields. Thirdly, to better and automatically understand the various classes of a segmented image we propose an original and reliable approach based on an energy-based model which allows us to deduce the most likely classes by using a set of identically partitioned segmentations (in the sense of a certain criterion) extracted from a learning database (with pre-interpreted classes) and a set of semantic likelihood (energy) term

The MAL family of proteins: Normal function, expression in cancer, and potential use as cancer biomarkers

The MAL family of integral membrane proteins consists of MAL, MAL2, MALL, PLLP, CMTM8, MYADM, and MYADML2. The best characterized members are elements of the machinery that controls specialized pathways of membrane traffic and cell signaling. This review aims to help answer the following questions about the MAL-family genes: (i) is their expression regulated in cancer and, if so, how? (ii) What role do they play in cancer? (iii) Might they have biomedical applications? Analysis of large-scale gene expression datasets indicated altered levels of MAL-family transcripts in specific cancer types. A comprehensive literature search provides evidence of MAL-family gene dysregulation and protein function repurposing in cancer. For MAL, and probably for other genes of the family, dysregulation is primarily a consequence of gene methylation, although copy number alterations also contribute to varying degrees. The scrutiny of the two sources of information, datasets and published studies, reveals potential prognostic applications of MAL-family members as cancer biomarkers—for instance, MAL2 in breast cancer, MAL2 and MALL in pancreatic cancer, and MAL and MYADM in lung cancer—and other biomedical uses. The availability of validated antibodies to some MAL-family proteins sanctions their use as cancer biomarkers in routine clinical practicePID2021-123179NB-I0

Targeted Quantitative Proteomic Profiling of Small GTPases in Cultured Cells and Tissues

Mass spectrometry (MS)-based bottom-up proteomics and quantitative proteomic labeling strategies have led to unprecedented insights into systems biology and provided invaluable resources as a multifaceted analytical tool. We have utilized such techniques to analyze small GTPases of the Ras superfamily, which represent a class of crucial signaling molecules in cells, and the aberrant regulation of their expressions is implicated with various types of human diseases. In this dissertation, I report the development and applications of novel targeted quantitative proteomic methods for high-throughput and reproducible profiling of small GTPases in cultured human cells and patient-derived brain tissues that carry disease-related changes.In Chapter 2, I describe the development of a novel scheduled multiple-reaction monitoring (MRM)-based targeted quantitative proteomic method, in conjunction with stable isotope labeling by amino acids in cell culture (SILAC) for the quantification of more than 90 small GTPases in the paired primary/metastatic melanoma cell lines. The data reveal previously unrecognized roles of RAB38 in promoting melanoma metastasis in vitro.In Chapter 3, the established scheduled MRM-based method was further applied to assess the differential expression of small GTPases in wild-type MCF-7 and the paired tamoxifen-resistant breast cancer cells. The method facilitated robust quantification of 96 small GTPases, among which down-regulation of RAB31 was analyzed further and demonstrated to play a role in the development of acquired tamoxifen resistance.In Chapter 4, we extended the use of the scheduled MRM method to comprehensively investigate the differential expression of small GTPases in paired primary/metastatic colorectal cancer cell (CRC) lines SW480 and SW620. With this approach, 83 small GTPases were robustly quantified, leading to the identification of SAR1B as a potential suppressor for CRC metastasis. We also showed that diminished SAR1B expression could stimulate epithelial–mesenchymal transition (EMT), thereby promoting motility and in vitro metastasis of SW480 cells.In Chapter 5, I describe the development of a novel targeted quantitative proteomic assay based on MRM and the use of crude synthetic stable isotope-labeled (SIL) peptides as internal standards (IS) and surrogate standards (SS). By using this approach, we quantified ~80 small GTPases from lysates of frontal cortex from post-mortem Alzheimer’s disease (AD) patient brain tissue samples. The method displayed excellent throughput, sensitivity and reproducibility. Furthermore, we observed that the protein expression levels of Rab3A/C, Rab4A/B and Rab27B proteins, which are involved with synaptic and secretory vesicles, increased with degree of disease severity. The MRM quantification results were further verified by Western blotting