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Beyond Classification: Structured Regression for Robust Cell Detection Using Convolutional Neural Network.

Yuanpu Xie1, Fuyong Xing2, Xiangfei Kong1

  • 1J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, FL 32611, USA.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|January 17, 2017
PubMed
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This study introduces a new convolutional neural network (CNN) model for accurate cell detection, even with touching cells and noisy images. The method uses weak annotations to identify cell centers effectively.

Area of Science:

  • Biomedical Image Analysis
  • Computational Biology
  • Machine Learning

Background:

  • Accurate cell detection is crucial for quantitative analysis in microscopy.
  • Existing methods struggle with touching cells, background noise, and size variations.
  • Weakly supervised learning offers a more efficient annotation approach.

Purpose of the Study:

  • To develop a novel convolutional neural network (CNN) based structured regression model for robust cell detection.
  • To address challenges like touching cells, background noise, and variations in cell size and shape.
  • To enable cell detection using minimal training data with weak annotations.

Main Methods:

  • A CNN-based structured regression model was developed.
  • The model generates 'proximity patches' indicating cell center likelihood.

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  • Proximity patches are fused to create a final proximity map for centroid identification.

    • Main Results:

    • The proposed method successfully handles touching cells, noise, and size variations.
    • Demonstrated superior performance compared to existing state-of-the-art methods.
    • Validated across three diverse datasets with different stains and modalities.

    Conclusions:

    • The novel CNN structured regression model offers a robust and efficient solution for cell detection.
    • Weak annotations significantly reduce the effort required for training cell detection models.
    • This approach advances biomedical image analysis by improving cell detection accuracy and reliability.