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Updated: Jun 23, 2026

Three-Dimensional Shape Modeling and Analysis of Brain Structures
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Three-Dimensional Shape Modeling and Analysis of Brain Structures

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Appearance and incomplete label matching for diffeomorphic template based hippocampus segmentation.

John Pluta1, Brian B Avants, Simon Glynn

  • 1Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA. jpluta@mail.med.upenn.edu

Hippocampus
|May 14, 2009
PubMed
Summary

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Developing Topics.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025

This study introduces a semiautomated method for segmenting the hippocampus in temporal lobe epilepsy. The approach uses incomplete labeling to improve accuracy and reliability in high-throughput studies.

Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Epilepsy Research

Background:

  • Accurate segmentation of the hippocampus is crucial for understanding temporal lobe epilepsy (TLE).
  • Existing automated methods often lack robustness, while manual segmentation is time-consuming and prone to variability.
  • High-throughput analysis requires efficient and reliable segmentation tools.

Purpose of the Study:

  • To develop a robust, high-throughput, semiautomated protocol for hippocampus segmentation in TLE.
  • To minimize user effort while maximizing the benefit of human input through incomplete labeling.
  • To improve robustness against inter- and intra-rater variability and labeling errors.

Main Methods:

  • A semiautomated, template-based protocol utilizing "incomplete labeling" was developed.

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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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Last Updated: Jun 23, 2026

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  • Partial hippocampus segmentation by users guides volumetric diffeomorphic normalization to a disease-specific template.
  • A probabilistic framework integrating label geometry and appearance data enhances robustness.

    • Main Results:

    • The framework demonstrated robustness to user labeling variability and errors.
    • Performance levels increased compared to fully automated approaches.
    • High inter-rater reliability was achieved, indicating consistent results.

    Conclusions:

    • The developed protocol is a reliable and efficient tool for hippocampus segmentation in TLE.
    • It does not require expert neuroanatomical training, making it accessible for broader research.
    • The method is suitable for high-throughput studies of both normal and atrophic hippocampi.