||The Computer Vision and Medical Image Analysis research group at Chalmers University of Technology is currently involved in several projects concerning medical applications, ranging from cardiac CT/CTA analysis within the SCAPIS project to PET imaging for early detection of Alzheimer's disease. In this talk, I’ll briefly present some of these ongoing research projects, where my main focus is to combine convolutional neural networks with explicit constraints on shape and deformation. This “best of both worlds” mindset aims for registration and segmentation methods utilizing the capability to learn high-level abstraction of CNNs while still taking anatomical plausibility into account.
BIOGRAPHY: Jennifer Alvén is a PhD student in the Computer Vision and Medical Image Analysis research group at the Dept. of Electrical Engineering, Chalmers University of Technology. Jennifer conducts research in the field of medical image analysis with Fredrik Kahl and Olof Enqvist as supervisors. Her research focus is machine learning and explicit/implicit shape models for registration and segmentation of medical 2D and 3D images. Examples of applications are pericardium segmentation, heart ventricle segmentation as well as coronary artery segmentation and stenosis detection. In addition to research, Jennifer is dedicated to popular science communication as well as to equality in academia (e.g. as as a project leader for WiSE, Women in SciencE, http://www.medtechwest.se/wise/).