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Three-Dimensional Shape Modeling and Analysis of Brain Structures
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Optic nerve head three-dimensional shape analysis.

Sunil Kumar Yadav1,2,3, Ella Maria Kadas1,2, Seyedamirhosein Motamedi1,2

  • 1Charité - Universitätsmedizin Berlin, NeuroCure Clinical Research Center, Corporate Member of Freie, Germany.

Journal of Biomedical Optics
|October 14, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for 3-D optic nerve head (ONH) shape analysis using optical coherence tomography (OCT). The technique reliably quantifies ONH structures for potential clinical applications.

Keywords:
bending energymesh surfaceoptic nerve headoptical coherence tomographyshape analysisvolume

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Biomedical Engineering

Background:

  • Noninvasive, high-resolution 3-D imaging of the optic nerve head (ONH) is achievable with spectral domain optical coherence tomography (OCT).
  • Quantifying ONH shape and extracting clinical information necessitates advanced analytical tools.

Purpose of the Study:

  • To develop and validate an automated method for 3-D optic nerve head (ONH) shape analysis using optical coherence tomography (OCT).
  • To enable computation of various 3-D parameters describing ONH morphology for clinical insights.

Main Methods:

  • Input: High-resolution 3-D OCT volume scans.
  • Segmentation of inner limiting membrane (ILM) and retinal pigment epithelium (RPE) to define surfaces.
  • Detection of Bruch's membrane opening (BMO) as the model origin.
  • 3-D surface reconstruction and computation of parameters like areas, volumes, and distances.
  • Calculation of ILM bending energy and 3-D BMO-MRW surface area.

Main Results:

  • Automated generation of a 3-D ONH model from OCT data.
  • Computation of multiple quantitative 3-D parameters characterizing ONH shape.
  • Demonstrated reliability and robustness across diverse ONH topologies.
  • Successful initial clinical application presented.

Conclusions:

  • The developed method provides a reliable and robust approach for 3-D ONH shape analysis using OCT.
  • The quantitative parameters derived can offer valuable information for clinical applications in ophthalmology.
  • This tool advances the analysis of ONH morphology from in vivo OCT data.