Recent Advances in Hypothalamic Segmentation for Neuroimaging: A Comprehensive Review

Area of Science:

• Neuroscience
• Medical Imaging
• Computational Biology

Background:

• The hypothalamus regulates vital physiological processes like energy balance and circadian rhythms.
• Its complex structure with distinct subnuclei necessitates precise segmentation for functional studies.
• Current research faces challenges due to a lack of unified segmentation protocols and pediatric data.

Purpose of the Study:

• To systematically review current hypothalamic segmentation methods.
• To discuss their applications in physiological and clinical research.
• To identify challenges and propose future directions for improved segmentation.

Main Methods:

• Categorization of segmentation approaches into anatomy-based manual and deep learning-based automated methods.
• Review of existing literature on hypothalamic segmentation techniques and their validation.
• Analysis of applications in physiological and clinical research contexts.

Main Results:

• Two primary segmentation types exist: manual (expert-driven, for validation) and automated (deep learning, for large-scale studies).
• Manual segmentation offers gold-standard accuracy for key subregions.
• Automated segmentation provides efficiency for exploring hypothalamic heterogeneity.

Conclusions:

• Standardized segmentation workflows are crucial for comparability and reproducibility.
• Addressing methodological gaps in pediatric hypothalamic segmentation is essential.
• Future efforts should focus on reducing bias and improving accuracy for comprehensive understanding.