  Whole brain segmentation with magnetic resonance imaging (MRI) enables the
non-invasive measurement of brain regions, including total intracranial volume
(TICV) and posterior fossa volume (PFV). Enhancing the existing whole brain
segmentation methodology to incorporate intracranial measurements offers a
heightened level of comprehensiveness in the analysis of brain structures.
Despite its potential, the task of generalizing deep learning techniques for
intracranial measurements faces data availability constraints due to limited
manually annotated atlases encompassing whole brain and TICV/PFV labels. In
this paper, we enhancing the hierarchical transformer UNesT for whole brain
segmentation to achieve segmenting whole brain with 133 classes and TICV/PFV
simultaneously. To address the problem of data scarcity, the model is first
pretrained on 4859 T1-weighted (T1w) 3D volumes sourced from 8 different sites.
These volumes are processed through a multi-atlas segmentation pipeline for
label generation, while TICV/PFV labels are unavailable. Subsequently, the
model is finetuned with 45 T1w 3D volumes from Open Access Series Imaging
Studies (OASIS) where both 133 whole brain classes and TICV/PFV labels are
available. We evaluate our method with Dice similarity coefficients(DSC). We
show that our model is able to conduct precise TICV/PFV estimation while
maintaining the 132 brain regions performance at a comparable level. Code and
trained model are available at:
https://github.com/MASILab/UNesT/tree/main/wholebrainSeg.
