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Multiscale segmentation- and error-guided iterative convolutional neural network for cerebral neuron segmentation in

Zhenzhen You1,2, Ming Jiang3, Zhenghao Shi1

  • 1Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China.

Microscopy Research and Technique
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PubMed
Summary

This study introduces a novel multiscale segmentation- and error-guided iterative convolutional neural network (MSEG-iCNN) for accurate neuron semantic segmentation in macaque brains. The MSEG-iCNN method significantly enhances neuron segmentation performance across diverse brain regions.

Keywords:
MSEG-iCNNNeuron semantic segmentationmacaque brainmicroscopic images

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Area of Science:

  • Neuroscience
  • Computer Vision
  • Biomedical Imaging

Background:

  • Accurate neuron semantic segmentation is crucial for extracting cerebral information like cell counting and morphometry.
  • Challenges in macaque brain neuron segmentation include complex structures, variable staining intensities, and imbalanced datasets.

Purpose of the Study:

  • To develop an advanced method for improving neuron semantic segmentation in macaque brains.
  • To address the difficulties posed by complex brain anatomy and staining variations.

Main Methods:

  • Proposed a multiscale segmentation- and error-guided iterative convolutional neural network (MSEG-iCNN).
  • Evaluated the method on microscopy images from 17 diverse anatomical regions of the macaque brain.

Main Results:

  • Achieved significant improvements in neuron semantic segmentation performance compared to existing methods (Random Forest, FCN-8s, U-Net, UNet++).
  • Demonstrated substantial performance gains, particularly for neurons with brighter staining intensities in specific regions like the lateral geniculate, globus pallidus, and hypothalamus.
  • The MSEG-iCNN method efficiently segments neurons across a wide range of staining intensities.

Conclusions:

  • The proposed MSEG-iCNN method effectively enhances neuron semantic segmentation in macaque brains.
  • Results are significant for downstream applications including neuron instance segmentation, morphological analysis, and disease diagnosis.