CellTracker

CellTracker automates single-cell segmentation and tracking in time-lapse microscopy to enable quantitative analysis of cellular behavior and molecular dynamics at single-cell resolution.

Key Features:

• Automated Segmentation and Tracking: Performs automated identification and temporal linking of individual cells across time-lapse image sequences.
• Image Pre-processing: Provides image pre-processing routines to improve image quality prior to segmentation and tracking.
• Manual Correction Tools: Allows manual correction of segmentation and tracking results to refine automated outputs.
• Statistical Analysis: Computes quantitative measurements including cell size and fluorescence intensity.
• Annotation: Supports annotation and labeling of cells and events within image series for downstream analysis.
• Model Training from Scratch: Enables training of models from scratch to adapt algorithms to specific datasets.
• Scalability and Customization: Offers scalability and customization options to accommodate diverse datasets and analysis needs.

Scientific Applications:

• Developmental Biology: Quantifies cell behaviors and lineage dynamics during developmental processes using time-lapse microscopy.
• Cancer Progression: Tracks tumor cell movement, proliferation, and phenotypic changes at single-cell resolution over time.
• Immunology: Monitors immune cell dynamics, interactions, and signaling-related fluorescence changes in live imaging.
• Time-lapse Microscopy Studies: Enables general studies of cellular behavior and molecular dynamics that rely on time-lapse microscopy data.

Methodology:

Implemented in Python and leveraging algorithms for image processing and machine learning for segmentation and tracking.