Fiduciary-Free Frame Alignment for Robust Time-Lapse Drift Correction Estimation in Multi-Sample Cell Microscopy.

Area of Science:

• Microscopy
• Image Analysis
• Computational Biology

Background:

• Frame alignment is crucial for analyzing cell and tissue dynamics in microscopic time-lapse observations.
• Multi-sample microscopy stage relocation causes region of interest (RoI) offset, jitter, and drift, leading to misaligned observations.
• Existing methods may struggle with the complexities of multi-sample stage movement.

Purpose of the Study:

• To develop a robust, automated approach for aligning frames in microscopic time-lapse observations.
• To compensate for stage drift and jitter introduced by sample stage relocation.
• To improve the accuracy of morphological and translational dynamics analysis in microscopy.

Main Methods:

• Utilized Recurrent All-Pairs Field Transforms (RAFT), a deep network architecture for optical flow.
• Implemented a sub-pixel precise alignment approach.
• Pre-trained the RAFT model on the Sintel dataset for registration tasks.

Main Results:

• The RAFT model achieved near-perfect precision for registration tasks on diverse time-lapse observations.
• The approach demonstrated robustness for elastically undistorted and translationally displaced microscopic images.
• The method successfully corrected for stage drift and jitter across multiple samples and devices.
• The approach was effective for image registration but not for tracking individual cells or contaminants.

Conclusions:

• The developed RAFT-based approach provides a robust and precise solution for aligning microscopic time-lapse frames.
• This method effectively compensates for stage drift and jitter, enhancing the reliability of microscopy data analysis.
• An open-source application is available for correcting stage drift and jitter in microscopy image sequences.