Various conditions -- brain diseases as well as systemic diseases -- affect the human brain's structural configuration. For each condition, this raises the question whether quantifying such changes through morphometric analysis of brain images can yield biomarkers for predicting diagnosis, disease progression, and therapy response.
I have developed image segmentation software (MAPER and Pincram, featured in IVA's 100-list of 2020, https://www.iva.se/projekt/research2business/ivas-100-lista-2020/) that automatically determines morphometric quantities on magnetic resonance images of the brain.
With this application, I seek an allocation of computation time and storage to conduct processing of images from several collaboration studies: CogThy (a controlled study of Graves' disease and its impact on the brain, PI Helena Filipsson-Nyström PhD, GU), Mega Donna Mega (a study of retinopathy in newborns; imaging arm led by Prof. Isabella Björkman-Burtscher, GU), Automated brain tissue classification on CT images (Dr. Michael Schöll), NeumRA (rheumatoid arthritis, an autoimmune condition of the synovial joints with secondary brain involvement; PI Prof. Maria Bokarewa, GU), Parkinson's disease (Rolf Heckemann). In addition, I am planning to carry out research projects (master's projects and similar) for methodological assessment and improvement. Among other image processing tasks, this will involve segmenting images of healthy volunteers from publicly available repositories (e.g. the Human Connectome Project) to obtain normative morphometric reference data.