bioscience-engine 0.2.0

Pure Python computational microscopy engine with OpenCV

BioScience Engine - Computational Microscopy Analysis

A comprehensive computational microscopy platform for automated cell tracking and analysis across diverse biological research domains. Built on Python with OpenCV, NumPy, SciPy, and Pandas for seamless integration into scientific workflows.

Key Features

• Automated cell detection and tracking across time-lapse sequences
• Multi-channel fluorescence analysis for protein expression and localization
• Cell division and apoptosis detection for proliferation studies
• Advanced morphology analysis quantifying shape and structural features
• Interactive visualization tools for result validation and exploration
• Batch processing capabilities for high-throughput screening

Primary Applications

Cancer Research

Track tumor cell migration, metastasis patterns, drug response testing, and quantify cell division rates under different treatment conditions.

Drug Discovery & Development

Automate high-throughput screening, generate dose-response curves, assess toxicity, and measure time-to-effect for compound evaluation.

Stem Cell Research

Monitor differentiation processes, track colony formation patterns, trace cell lineages, and ensure quality control in biomanufacturing.

Immunology Studies

Analyze T-cell migration, study cell-cell interactions, monitor inflammatory responses, and evaluate vaccine efficacy.

Wound Healing & Regeneration

Measure cell migration speeds, study collective cell movement patterns, and test tissue repair strategies.

Neuroscience

Track neuron development, monitor synaptic plasticity, study neurodegeneration progression, and test neuroprotective compounds.

Microbiology

Analyze bacterial growth dynamics, study antibiotic resistance, track cell division patterns, and monitor biofilm formation.

Installation

git clone https://github.com/yourusername/bioscience-engine
cd bioscience-engine
pip install -e .

Dependencies

• OpenCV 4.5+
• NumPy 1.21+
• SciPy 1.7+
• Pandas 1.3+
• Matplotlib 3.4+

Usage Example

import bioscience_engine as bio

# Load and analyze time-lapse data
pipeline = bio.Pipeline()
pipeline.load_images("experiment_01/")
pipeline.set_parameters(
    min_cell_size=100,
    detection_threshold=0.8
)

# Execute complete analysis workflow
pipeline.denoise()
pipeline.detect_cells()
trajectories = pipeline.track_cells()

# Access comprehensive results
print(f"Tracked {len(trajectories)} cells across {trajectories.time_points} frames")
print(f"Detected {trajectories.division_events} cell divisions")
print(f"Identified {trajectories.apoptosis_events} cell death events")

# Export for further analysis
trajectories.export_csv("cell_analysis_results.csv")
trajectories.generate_report("experiment_summary.pdf")

Analysis Pipeline

The platform provides a complete workflow for computational microscopy:

Image Loading -> Preprocessing -> Cell Detection -> Tracking -> Feature Extraction -> Visualization
       |              |              |            |             |                  |
   Multiple      Denoising      Segmentation  Trajectory    Morphology      Interactive
   Formats       Registration   Multi-scale   Linking       Statistics      Plots & Videos

Core Analysis Modules

• Cell Detection: Multi-scale segmentation adapting to varying cell sizes and densities
• Tracking Algorithm: Robust trajectory linking handling cell divisions and apoptosis
• Morphology Analysis: Quantitative shape descriptors including area, circularity, eccentricity
• Multi-channel Analysis: Fluorescence quantification and colocalization studies
• Statistical Analysis: Built-in hypothesis testing and effect size calculations

Advanced Capabilities

Cell Division Detection

Automatically identify mitosis events and establish parent-daughter relationships with precise timing of division events.

Apoptosis Detection

Monitor cell death through shrinkage, membrane blebbing, intensity changes, and fragmentation patterns.

Multi-Channel Support

Analyze fluorescence across brightfield, GFP, RFP, DAPI and other channels for comprehensive protein expression studies.

Interactive Correction Tools

User-friendly interface for manual track editing, cell addition/deletion, and result validation.

Output & Export

• Trajectory Data: Complete cell tracks with speed, direction, and persistence metrics
• Event Logs: Division and apoptosis events with timing and confidence scores
• Morphology Reports: Quantitative shape analysis for each cell over time
• Statistical Summaries: Condition comparisons with p-values and effect sizes
• Visualization: Track overlays, migration plots, and event annotations
• Multiple Formats: CSV, Excel, HDF5 for integration with other analysis tools

Designed for Researchers

Biologist-Friendly

• Intuitive Python API requiring minimal programming experience
• Interactive GUI for manual correction and exploration
• Comprehensive documentation with example datasets
• Pre-configured analysis pipelines for common experiments

Computational Flexibility

• Modular architecture for custom analysis workflows
• Easy integration with existing Python scientific stacks
• Extensible framework for adding new algorithms
• Compatibility with common microscopy data formats

Contributing

We welcome contributions from the scientific community. Please see our contributing guidelines for:

• Code standards following PEP 8
• Testing requirements for new features
• Documentation updates
• Example datasets and use cases

License

MIT License - see LICENSE file for complete details.

Authors

• Elijah Manda - Initial implementation and core architecture

Acknowledgments

This project builds upon open-source scientific Python libraries including OpenCV, NumPy, SciPy, Pandas, and the broader scientific Python ecosystem.

---

Empowering biological discovery through computational microscopy