Building a high-resolution computational atlas of the whole human brain with histology, deep learning, and Bayesian modeling: open-source implementation and application to neuroimaging studies
Widespread neuroimaging packages like FreeSurfer, FSL or SPM enable morphometric, functional, and connectivity studies of the human brain in vivo using MRI. While these packages are frequently updated to stay near the state of the art in terms of methodology, they still rely on computational atlases which are over a decade old using in vivo MRI scans, and which fail to describe the human brain beyond the whole structure level. In this talk, I will present ongoing work to build a computational atlas of the whole human brain at the substructure level, which we intend to integrate with FreeSurfer. Specifically, I will present work on Bayesian atlas building with ex vivo data; 3D reconstruction of histology with Bayesian and deep learning methods; and sequence-independent segmentation of in vivo brain MRI with a combination of deep learning and Bayesian inference. I will also present preliminary results on population studies of the hippocampus and amygdala, as well as 3D reconstructions of both private and public ex vivo datasets (Allen Institute, BigBrain).