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Generative frame interpolation enhances tracking of biological objects in time-lapse microscopy.

Swaraj Kaondal1, Arsalan Taassob1, Sara Jeon2

  • 1Department of Plant and Microbial Biology, North Carolina State University, Raleigh, USA.

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Summary

Generative video frame interpolation enhances microscopy image temporal resolution, improving object tracking accuracy across diverse biological samples without algorithm retraining. This approach facilitates complex trajectory analysis in time-lapse microscopy.

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Area of Science:

  • Computational Biology
  • Biophysics
  • Image Analysis

Background:

  • Object tracking in microscopy is vital for biological process elucidation.
  • Current tracking methods often necessitate dataset-specific algorithm fine-tuning.
  • An alternative is augmenting image datasets to better suit existing tracking algorithms.

Purpose of the Study:

  • To investigate generative video frame interpolation for augmenting temporal resolution in time-lapse microscopy.
  • To assess if frame interpolation can facilitate object tracking across various biological contexts.
  • To evaluate the efficacy of different frame interpolation models on microscopy data.

Main Methods:

  • Systematic comparison of Latent Diffusion Model for Video Frame Interpolation (LDMVFI), Real-time Intermediate Flow Estimation (RIFE), Compression-Driven Frame Interpolation (CDFI), and Frame Interpolation for Large Motion (FILM).
  • Application of these models to interpolate frames in time-lapse microscopy datasets including nuclei, bacteria, yeast, cancer cells, and organoids.
  • Evaluation of generated images using structural image similarity and segmentation comparison with real images.
  • Assessment of tracking performance improvement using a mask overlap-based algorithm on original and interpolated datasets.

Main Results:

  • Off-the-shelf frame interpolation algorithms generated bio-realistic interpolated microscopy images without retraining.
  • High structural image similarity and comparable segmentations were achieved between real and interpolated images.
  • Frame interpolation significantly enhanced object tracking across multiple datasets.
  • Complex trajectories, previously unresolvable, were captured after frame interpolation.
  • Minimal parameter tuning was required for improved tracking.

Conclusions:

  • Generative frame interpolation offers a powerful method to improve object tracking in time-lapse microscopy.
  • This technique enhances temporal resolution and facilitates the analysis of complex biological dynamics.
  • Findings suggest the potential for developing generalist microscopy tracking algorithms by combining deep learning segmentation with generative frame interpolation.