Nonhuman Primate Brain Tissue Segmentation Using a Transfer Learning Approach
Journal:
arXiv
Published Date:
Mar 28, 2025
Abstract
Non-human primates (NHPs) serve as critical models for understanding human
brain function and neurological disorders due to their close evolutionary
relationship with humans. Accurate brain tissue segmentation in NHPs is
critical for understanding neurological disorders, but challenging due to the
scarcity of annotated NHP brain MRI datasets, the small size of the NHP brain,
the limited resolution of available imaging data and the anatomical differences
between human and NHP brains. To address these challenges, we propose a novel
approach utilizing STU-Net with transfer learning to leverage knowledge
transferred from human brain MRI data to enhance segmentation accuracy in the
NHP brain MRI, particularly when training data is limited. The combination of
STU-Net and transfer learning effectively delineates complex tissue boundaries
and captures fine anatomical details specific to NHP brains. Notably, our
method demonstrated improvement in segmenting small subcortical structures such
as putamen and thalamus that are challenging to resolve with limited spatial
resolution and tissue contrast, and achieved DSC of over 0.88, IoU over 0.8 and
HD95 under 7. This study introduces a robust method for multi-class brain
tissue segmentation in NHPs, potentially accelerating research in evolutionary
neuroscience and preclinical studies of neurological disorders relevant to
human health.
