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Phasetime: Deep Learning Approach to Detect Nuclei in Time Lapse Phase Images.

Pengyu Yuan1, Ali Rezvan2, Xiaoyang Li3

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This study demonstrates that phase microscopy images can identify and track cell nuclei without fluorescent labeling. This novel approach avoids potential cell perturbations, enabling label-free nuclei detection for time lapse imaging.

Keywords:
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Area of Science:

  • Cell biology
  • Microscopy techniques
  • Bioimaging

Background:

  • Time lapse microscopy is crucial for observing cellular dynamics.
  • Fluorescent labeling is commonly used but can interfere with cell function and imaging.
  • Developing label-free methods is essential for accurate biological quantification.

Purpose of the Study:

  • To investigate the potential of phase images for identifying and tracking cell nuclei.
  • To develop a deep learning model for nuclei segmentation using only phase contrast microscopy.
  • To enable label-free nuclei detection in time lapse imaging.

Main Methods:

  • Utilized traditional blob detection for initial nuclei mask generation.
  • Employed the Mask RCNN deep learning model for nuclei detection and segmentation.
  • Trained the model using phase images, without requiring ground truth masks during training.

Main Results:

  • Achieved an average precision of 0.82 for nuclei detection at an IoU threshold of 0.5.
  • Obtained a mean IoU of 0.735 between phase-image-generated masks and expert-annotated ground truth masks.
  • Successfully segmented nuclei solely from phase images, validating the hypothesis.

Conclusions:

  • Phase images contain sufficient information for accurate nuclei detection and tracking.
  • The developed deep learning approach enables label-free nuclei segmentation, overcoming limitations of fluorescent tagging.
  • This method offers a promising alternative for live-cell imaging and quantitative biological studies.