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A brain subcortical segmentation tool based on anatomy attentional fusion network for developing macaques.

Tao Zhong1, Ya Wang2, Xiaotong Xu1

  • 1School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, China.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|June 13, 2024
PubMed
Summary

This study introduces the Anatomy Attentional Fusion Network (AAF-Net) for precise macaque brain subcortical segmentation using multimodal MRI data. The novel method enhances neuroimaging analysis for understanding brain development and diseases.

Keywords:
Anatomy constraintBrain sMRIDeveloping macaqueDevelopmental analysisSubcortical segmentation

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Primate Brain Anatomy

Background:

  • Accurate measurement of macaque brain subcortical structures is vital for neuroscience research.
  • Existing human neuroimaging tools face challenges with macaque brain differences.
  • Juvenile macaque brains present dynamic developmental and structural variations impacting segmentation.

Purpose of the Study:

  • To develop a novel computational tool for precise subcortical segmentation in macaque brains.
  • To address challenges in segmenting dynamic, heterogeneous, and age-varying macaque brain structures.
  • To enhance the understanding of neurodevelopmental processes and neurodegenerative diseases in primates.

Main Methods:

  • Anatomy Attentional Fusion Network (AAF-Net) integrating multimodal MRI and anatomical constraints.
  • Utilized Signed Distance Maps (SDMs) as anatomical constraints for refined segmentation.
  • Developed and validated the tool using over 700 macaque MRIs from 19 datasets, including longitudinal data.

Main Results:

  • AAF-Net achieved precise subcortical segmentation in macaque brains.
  • The tool demonstrated robust performance through four-fold cross-validation on manually labeled datasets.
  • External datasets confirmed the tool's generalization capabilities for brain development research.

Conclusions:

  • AAF-Net offers an effective solution for accurate macaque subcortical segmentation.
  • The open-source tool facilitates advancements in primate neuroimaging and developmental studies.
  • This work contributes to a deeper understanding of primate brain structure and function.