About the use of non-imaging data to improve domain adaptation for spinal cord segmentation on MRI
Benoît Sauty De Chalon

Meeting • 26 November 2019

Abstract
Currently in my last year of Master in Bioinformatics in Paris, I have come to give this presentation in order to present myself to the lab, my past work and centers of interest. If my profile fits the spirit of the lab, I hope to be able to pursue a Master's internship and then a PhD at the BIG. After a brief overview of the classes I took during my studies and the projects that are relevant to medical imaging and computer vision, I will more specifically expand on the research internship I did last year at the NeuroPoly lab in Montreal, that specializes in spinal cord MRI analysis. My task was to improve the segmentation models in order to be able to perform on data from unseen domains (new acquisition sequence, new scanner, new contrast, etc). To do this, the idea was to give a physical a priori to the model by inputing acquisition metadata along with the image and perform feature wise linear modulations to the feature maps in the segmentation CNN. I will also present some of the initiatives I took outside of this project for the lab workflow. Finally, I will present my interest for the work done at the BIG and why I would like to work there.