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Automatic neuron segmentation and neural network analysis method for phase contrast microscopy images.

Jincheng Pang1, Nurdan Özkucur2, Michael Ren3

  • 1Deptment of Electrical and Computer Engineering, Tufts University, Medford, MA, 02155, USA.

Biomedical Optics Express
|November 25, 2015
PubMed
Summary
This summary is machine-generated.

Phase Contrast Microscopy (PCM) provides long-term cell imaging without photobleaching. A new method uses a parametric model and level sets to simultaneously restore and segment neuron images, overcoming PCM artifacts.

Keywords:
(100.0100) Image processing(100.1830) Deconvolution(100.2960) Image analysis(100.3020) Image reconstruction-restoration(100.3190) Inverse problems

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Area of Science:

  • Biomedical Imaging
  • Cell Biology
  • Image Analysis

Background:

  • Phase Contrast Microscopy (PCM) enables long-term living cell studies.
  • PCM contrast relies on natural refractive index variations, avoiding photobleaching.
  • Raw PCM images contain complex artifacts, especially in neuron imaging (somas and dendrites).

Purpose of the Study:

  • To develop a novel method for artifact correction and segmentation in PCM neuron images.
  • To address the distinct artifact patterns observed in neuron cell bodies and dendrites.
  • To create an automated image analysis pipeline for PCM data.

Main Methods:

  • Introduced a novel parametric image model.
  • Utilized the level set framework for image restoration and segmentation.
  • Developed an associated variational approach for simultaneous processing.

Main Results:

  • Successfully restored and segmented neuron images from PCM data.
  • Demonstrated advantages over existing methods using synthetic and real images.
  • Validated an automated image analysis pipeline based on the novel technique.

Conclusions:

  • The proposed parametric model and level set approach effectively overcome PCM artifacts in neuron images.
  • Simultaneous restoration and segmentation improve the accuracy of cell body and dendrite analysis.
  • The automated pipeline offers a significant advancement for PCM-based cell studies.