Juan Eugenio Iglesias, PhD: Bayesian Atlas Building and Segmentation of Brain MRI


Monday, December 16, 2019, 4:00pm to 5:00pm


Ramzi Cotran Conference Center Amory 3, 75 Francis Street, Brigham and Women’s Hospital
Juan Eugenio Iglesias

Juan Eugenio Iglesias, PhD
Research Staff, Martinos Center for Biomedic al Imaging (MGH), Lecturer in Radiology, Harvard Medical School Research Affiliate, CSAIL, MIT


In this talk, I will present our work on Bayesian segmentation methods for brain MRI analysis. Compared with modern deep learning approaches, these methods have two advantages: They enable us to use ex vivo imaging (e.g., ex vivo MRI, histology), of superior resolution and contrast, to build very detailed atlases; and they are agnostic to the MRI contrast of the input scan to analyze, even if multimodal. First, I will introduce a generative model for brain anatomy, and how we can use Bayesian inference and model selection to build computational atlases within the model given a set of manual segmentations, while accounting for the consistency of brain structures across subjects and the number of training examples. Second, I will explain how the probabilistic model can be extended to generate image intensities, and how this new model can also be “inverted” with Bayesian inference to produce automated segmentations. Finally, I will present some results on segmentation of subregions of the hippocampus, amygdala, and thalamus.

See also: ABCSeries