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Related Experiment Video

Updated: May 31, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Automatic segmentation of neonatal images using convex optimization and coupled level sets.

Li Wang1, Feng Shi, Weili Lin

  • 1Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.

Neuroimage
|July 19, 2011
PubMed
Summary

This study introduces a new method for segmenting neonatal brain MR images, improving accuracy by combining intensity, spatial, and thickness information. The technique simultaneously segments tissues and reconstructs cortical surfaces, showing promising results in validation.

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Area of Science:

  • Medical Imaging
  • Neuroscience
  • Computer Vision

Background:

  • Neonatal brain MRI segmentation is difficult due to low resolution, inverted white/gray matter contrast, and intensity inhomogeneity.
  • Existing atlas-based and voxel-wise methods have limitations for neonatal brain analysis.
  • Active contour/surface models are underutilized in neonatal brain segmentation.

Purpose of the Study:

  • To develop a novel method for accurate neonatal brain MR image segmentation.
  • To integrate local intensity, atlas priors, and cortical thickness constraints within a level-set framework.
  • To achieve simultaneous tissue segmentation and cortical surface reconstruction.

Main Methods:

  • A novel level-set framework combining local intensity, atlas spatial prior, and cortical thickness constraint.

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Last Updated: May 31, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

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14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

  • Robust tissue surface initialization using convex optimization.
  • Simultaneous segmentation and surface reconstruction.

    • Main Results:

    • The proposed method demonstrated promising results on a large neonatal dataset.
    • Validation on 10 manually segmented neonatal brain images confirmed the method's effectiveness.
    • Simultaneous segmentation and surface reconstruction were successfully achieved.

    Conclusions:

    • The novel level-set method offers an effective approach for neonatal brain MR image segmentation.
    • Integrating multiple constraints improves accuracy and robustness.
    • The method facilitates simultaneous tissue segmentation and cortical surface reconstruction.