Analyse et traitement d'images multi modales en oncologie

Field: Computer Science (Physical Sciences)

With an initial formation in theoretical computer sciences with a focus on image processing and analysis, my current research activities deal with image and information processing and analysis for applications in medicine, namely oncology and radiotherapy. More specifically, my research interests are image automatic segmentation and classification for organs and tumors delineation, image denoising and deconvolution for qualitative and quantitative improvement, and more recently, multi observation models for multi modal imaging and information fusion for computer-aided decision making in patients management. These developments are especially considered within the context of the use of Positron Emission Tomography and Computed Tomography (PET/CT) for oncology and radiotherapy applications. My research activities have been and are still carried out within the team "quantitative multi modal imaging for diagnosis and therapy", in the LaTIM INSERM U1101. This framework ensures a multi disciplinary teamwork, in collaboration with radiation oncologists, nuclear medicine physicians, physicists, engineers, mathematicians and computer scientists. As a PhD student, my main contribution to the field has been the development of image segmentation algorithms dedicated to the automated delineation of metabolically active tumor volumes in PET images, with a specific focus on adapting the methodology to specific characteristics of the processed images. As a post-doctoral fellow, I have been further investigating the accuracy, robustness and reproducibility of this methodology within a project for which I had obtained funding from the French research agency (ANR) for two and a half years. I have also been investigating the impact of such methodology and its resulting tumor volumes measurements in various applications such as the dosimetry impact in radiotherapy treatment planning or patient management and therapy assessment in oncology. During the last six years I have also been more and more involved in research developments by several PhD students and post-doctoral fellows, such as PET images fusion for quantitative follow up, realistic simulations and dosimetry evaluation, methodologies for reduction of partial volume effects and noise in emission imaging, and textural features analysis for characterization of tracer uptake heterogeneity within tumors. Overall, I have been acting as co-supervisor of most of the trainees and PhD students of the team under the direction of Research Director D. Visvikis. This responsibility included weekly meetings and discussions with the students, help with designing experiments and analyzing the results, writing of thesis reports and research articles, as well as finding solutions to theoretical problems and technical issues. In the next few years, my research project will be focused on the development of a robust and flexible framework for the modeling and the automatic analysis of multi modality, multi resolution, multi observation and multi temporal images datasets, such as PET/CT, SPECT/CT, PET/MRI, multi MRI or PET tracers imaging, as well as dynamic acquisitions. This development will allow deriving new predictive models and decision tools by fusion of the available multi source information (imaging, genetics and other clinical data), validated in various models such as esophageal, rectal, lung, or head & neck cancers. Additional applications in neurology (for example Alzheimer's disease) might also be explored. This project will be based on previous developments by PhD students that are almost finished with their thesis, and will focus on new PhD students beginning their work under my supervision in October 2011 and early 2012, whose recruitment was possible thanks to grants I contributed to obtain in 2010-2011.