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FitEllipsoid: a fast supervised ellipsoid segmentation plugin.

Bastien Kovac1, Jérôme Fehrenbach2,3, Ludivine Guillaume1

  • 1ITAV, CNRS, Université de Toulouse, 1 Pl. Pierre Potier, Toulouse, 31106, France.

BMC Bioinformatics
|March 17, 2019
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Summary
This summary is machine-generated.

FitEllipsoid software simplifies 3D image segmentation for ellipsoidal shapes. This supervised method requires minimal user interaction, enabling faster and more accurate analysis of biological samples like nuclei in tumor spheroids.

Keywords:
EllipsoidIcy pluginSupervised segmentation

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Area of Science:

  • Computational biology
  • Image analysis
  • Biophysics

Background:

  • 3D image segmentation is challenging with low contrast or small object distances.
  • Existing supervised methods demand extensive user input, such as delineating objects across all 2D slices.

Purpose of the Study:

  • To develop a user-friendly, supervised software for segmenting ellipsoidal shapes in 3D images.
  • To reduce user interaction time and improve segmentation accuracy compared to traditional methods.

Main Methods:

  • Introduced FitEllipsoid, a supervised segmentation code utilizing minimal user input (clicks on object boundaries in orthogonal views).
  • Employs an original computational approach for affine-invariant ellipsoid fitting to point clouds.
  • Validated by segmenting numerous 3D nuclei in tumor spheroids.

Main Results:

  • FitEllipsoid enables segmentation of hundreds of ellipsoidal shapes with high accuracy and minimal interaction.
  • Quantitative geometric segmentation results can be exported as CSV files or binary images.
  • Significantly faster segmentation compared to 2D slice-by-slice delineation.

Conclusions:

  • FitEllipsoid is a user-friendly, open-source plugin for the Icy image analysis software.
  • Facilitates direct analysis of biological samples and generation of segmentation databases for machine learning.
  • A complementary Matlab toolbox is available via GitHub.