LivecellX¶
LivecellX is a deep-learning-based, single-cell object-oriented framework designed for quantitative analysis of single-cell dynamics in long-term, label-free live-cell imaging datasets.
Manuscript¶
For detailed methods and validations, please see our manuscript:
> Ni et al., LivecellX: A Deep-learning-based, Single-Cell Object-Oriented Framework for Quantitative Analysis in Live-Cell Imaging (__stub__, under review).
Key Features¶
Segmentation & Tracking: Accurate deep learning-based segmentation, integrated tracking algorithms (SORT, b-track).
Corrective Segmentation Network (CS-Net): Automatically correct over- and under-segmentation errors using context-aware deep learning models.
Trajectory-Level Correction: Algorithms utilizing temporal consistency for error correction and accurate lineage reconstruction.
Biological Process Detection: Automated detection and classification of cellular processes (mitosis, apoptosis).
High-dimensional Feature Extraction: Including morphological (Active Shape Models), textural (Haralick, LBP), and deep learning-based features (VAE).
Object-Oriented Data Structure: Intuitive and efficient management of single-cell trajectories and features, enabling multi-dataset integration.
Napari GUI Integration: Interactive visualization, manual correction, and lineage tracing.
Parallelized Computation: Efficient processing of large datasets using multi-core computation.
Getting Started¶
Explore our [Tutorials](livecellx_notebooks/tutorials/index) and [Examples](livecellx_notebooks/examples/index).
Contribute via [GitHub](https://github.com/xing-lab-pitt/livecellx).