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  1. Home
  3. Recent Advances In Hypothalamic Segmentation For Neuroimaging: A Comprehensive Review.
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  3. Recent Advances In Hypothalamic Segmentation For Neuroimaging: A Comprehensive Review.

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Recent Advances in Hypothalamic Segmentation for Neuroimaging: A Comprehensive Review.

J Z Hu1, Z P Chen1, C Han1

  • 1From the Department of Radiology, The First Hospital of Jilin University, Changchun, Jilin, China (J.Z.H., Z.P.C., X.C.P., D.T.); and the Reproductive Medicine Center, Prenatal Diagnosis Center, First Hospital of Jilin University, Changchun, China (C.H.).

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View abstract on PubMed

Summary
This summary is machine-generated.

This review summarizes methods for segmenting the hypothalamus, a key brain region. Standardized approaches are needed for accurate analysis of its complex structure and function, especially in children.

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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.