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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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Anisotropic ssTEM image segmentation using dense correspondence across sections.

Dmitry Laptev1, Alexander Vezhnevets, Sarvesh Dwivedi

  • 1Department of Computer Science, ETH Zurich, Switzerland. dlaptev@inf.ethz.ch

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|January 5, 2013
PubMed
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This study presents an automatic method for neuron membrane segmentation in electron microscopy images. By using neighboring section context, the approach improves accuracy for connectomics research.

Area of Science:

  • Neuroscience
  • Computational Biology
  • Biomedical Imaging

Background:

  • Connectomics aims to map neural connections using high-resolution ssTEM imagery.
  • Neuron geometry reconstruction from histological slides is crucial for connectomics.
  • Accurate neuronal segmentation is a key challenge in processing ssTEM data.

Purpose of the Study:

  • To develop an automatic membrane segmentation method for anisotropic electron microscopy brain tissue sections.
  • To improve neuronal segmentation accuracy by incorporating information from adjacent sections.
  • To enhance the process of neuron geometry reconstruction for connectomics.

Main Methods:

  • Utilized SIFT Flow algorithm to establish global dense correspondence between adjacent histological sections.

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  • Evaluated features of corresponding pixels to resolve segmentation ambiguities.
  • Implemented an automatic membrane segmentation approach leveraging multi-section context.

    • Main Results:

    • The proposed method demonstrated improved accuracy in neuronal segmentation compared to single-section algorithms.
    • Achieved 3.6% and 6.4% higher accuracy based on two distinct metrics.
    • Successfully resolved ambiguities in neuronal segmentation by integrating contextual information.

    Conclusions:

    • Contextual information from neighboring sections significantly enhances automatic neuronal segmentation.
    • The developed method offers a more accurate approach for neuron geometry reconstruction in connectomics.
    • This technique advances the capabilities for large-scale brain mapping and understanding neural circuitry.