I'm currently as a Ph.D. student at LaBRI - University of Bordeaux (Image and Sound team, Pictura group).
My research subject is the development of methods for the early detection of Alzheimer's disease under the supervision of Dr. Pierrick Coupé.
Recently, many studies have proposed biomarkers to perform early detection of Alzheimer's disease. Some of them have proposed methods based on inter-subject similarity while other approaches have investigated framework using intra-subject variability. In this work, we propose a novel framework combining both approaches within an efficient graph of brain structures grading. Subsequently, we demonstrate the competitive performance of the proposed method compared to state-of-the-art methods.
The aim of our work is to study the efficiency of hippocampal subfields compared to the whole hippocampus structure with a multimodal patch-based framework that enables to capture subtler structural and microstructural alterations. To this end, we analyze the significance of the different hippocampal subfields for AD diagnosis and prognosis with volumetric, diffusivity measurements and a novel multimodal patch-based grading framework that combines structural and diffusion MRI. The experiments conducted in this work showed that the use of the whole hippocampus provides the most discriminant biomarkers for advanced AD detection while biomarkers applied into subiculum obtain the best results for AD prediction, improving by 2% the accuracy compared to the whole hippocampus.
We study the potential using texture information based on Gabor filters to improve patch-based grading method performance, with a focus on the hippocampal structure. We also propose a novel fusion framework to efficiently combine multiple grading maps derived from a Gabor filters bank. Finally, we compare our new texture-based grading biomarker with the state-of-the-art approaches to demonstrate the high potential of the proposed method.
We propose to use a similar framework on different diffusion parameters extracted from DTI. We also propose to use a fast patch-based search strategy to provide novel biomarkers for the early detection of AD. We intensively compare our new grading-based DTI features with basic MRI/DTI biomarkers and evaluate our method within a cross validation classification framework. Finally, we demonstrate that the proposed biomarkers obtain competitive results for the identification of the different stages of AD.
To date, the evolution of aortic remodeling, following a type B dissection, is still unknown. Physicians need new tools to extract parameters describing the morphology of aorta and the properties of the dissection such as the volume, and the position of the different tears. In this work, we propose a new method for the aorta segmentation and the extraction of medical parameters related to the aortic dissection (i.e., the volume of the true and the false lumen, position and morphology of the tears).
Master thesis [In French]