LivecellX: A Scalable Deep Learning Framework for Single-Cell Object-Oriented Analysis in Live-Cell Imaging.

Area of Science:

• Biotechnology
• Computational Biology
• Cell Biology

Background:

• Accurate quantitative analysis of single-cell dynamics in live-cell imaging is crucial for understanding cellular heterogeneity, disease mechanisms, and drug responses.
• Segmentation and tracking errors in live-cell imaging can cascade, significantly impacting trajectory analyses despite recent advances.
• Existing methods struggle with precise cell segmentation and tracking, limiting the reliability of downstream analyses.

Purpose of the Study:

• To introduce LivecellX, a deep-learning-based, object-oriented framework for scalable live-cell dynamics analysis.
• To address segmentation errors (over- and under-segmentation) through novel correction techniques and evaluation metrics.
• To provide a robust infrastructure for high-throughput single-cell imaging analysis, including feature extraction and lineage reconstruction.

Main Methods:

• Developed a Corrective Segmentation Network (CS-Net) using normalized distance transforms and synthetic augmentation to rectify segmentation inaccuracies.
• Implemented trajectory-level correction algorithms leveraging temporal consistency and CS-Net.
• Annotated a novel live-cell imaging dataset from two distinct microscope types for training and validation.
• Designed an object-oriented framework with Napari GUI support and parallelized computation for efficient data management and analysis.

Main Results:

• The CS-Net effectively corrects segmentation inaccuracies, reducing errors at both image and trajectory levels.
• LivecellX demonstrates robust performance across different datasets and imaging platforms.
• The framework facilitates biological process detection, feature extraction, and lineage reconstruction.
• LivecellX provides a scalable and extensible infrastructure for high-throughput single-cell imaging analysis.

Conclusions:

• LivecellX offers a significant advancement in analyzing live-cell dynamics by accurately correcting segmentation and tracking errors.
• The framework enhances the reliability and scalability of single-cell imaging analysis, supporting biological discovery.
• LivecellX lays the foundation for future developments in live-cell imaging analysis and foundation models.