automorphotrack 2.1.0

Automated pipeline for mitochondrial and lysosomal detection, tracking, morphology, and colocalization analysis in microscopy images.

AutoMorphoTrack

AutoMorphoTrack is a comprehensive, modular image-analysis pipeline for automated detection, morphology classification, shape profiling, motility tracking, and colocalization analysis of mitochondria and lysosomes in multichannel fluorescence microscopy data.

Developed by Armin Bayati, Ph.D.

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Overview

AutoMorphoTrack processes time-lapse .tif stacks (typically two-channel: mitochondria + lysosomes) and generates publication-ready visual and quantitative outputs at every analysis step. The pipeline integrates seamlessly into scientific workflows and prioritizes transparency, reproducibility, and ease of use.

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Key Features

• Automated organelle detection with morphological filtering
• Integrated tracking of mitochondria and lysosomes across time
• Shape profiling with publication-ready violin plots
• Motility analysis with velocity and displacement quantification
• Colocalization metrics (Manders, Pearson coefficients)
• Comprehensive validation tools for segmentation and tracking accuracy
• Colorblind-safe visualizations throughout
• High-resolution outputs (600 DPI publication-ready figures)

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Installation

Via PyPI (Recommended)

pip install automorphotrack

From Source

git clone https://github.com/abayatibrain/AutoMorphoTrack.git
cd AutoMorphoTrack
pip install -e .

Development Install

git clone https://github.com/abayatibrain/AutoMorphoTrack.git
cd AutoMorphoTrack
pip install -e ".[dev]"

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Quick Start

from automorphotrack import (
    detect_organelles,
    count_lysosomes_per_frame,
    classify_morphology,
    analyze_shape_features,
    profile_shape_data,
    track_organelles,
    track_overlay,
    analyze_motility,
    analyze_colocalization,
    summarize_integrated_data
)

tif_path = "path/to/Composite.tif"

# Run full pipeline
detect_organelles(tif_path)
count_lysosomes_per_frame(tif_path)
classify_morphology(tif_path)
analyze_shape_features(tif_path)
profile_shape_data()
track_organelles(tif_path)
track_overlay(tif_path)
analyze_motility()
analyze_colocalization(tif_path)
summarize_integrated_data()

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Module Overview

Module	Function	Inputs	Outputs	Purpose
detection	detect_organelles()	TIF file	PNG, MP4, masks	Segment mitochondria and lysosomes
lyso_count	count_lysosomes_per_frame()	TIF file	PNG, CSV, MP4	Quantify lysosomes per frame
morphology	classify_morphology()	TIF file	PNG, CSV, MP4	Classify mitochondria as elongated vs. punctate
shape_features	analyze_shape_features()	TIF file	PNG, CSV	Extract circularity, solidity, aspect ratio
shape_profiling	profile_shape_data()	CSVs	PNG, CSV	Generate combined violin plots
tracking	track_organelles()	TIF file	PNG, CSV, MP4	Track organelle trajectories
tracking_overlay	track_overlay()	TIF file	PNG, CSV, MP4	Overlay tracks on intensity images
motility	analyze_motility()	CSVs	PNG, CSV	Compute velocity and displacement
colocalization	analyze_colocalization()	TIF file	PNG, CSV, MP4	Quantify mitochondrial-lysosomal overlap
summary	summarize_integrated_data()	CSVs	PNG, CSV	Correlation matrix across all metrics
validation	validate_segmentation()	Masks	dict	Compute Dice, IoU, F1, precision, recall
validation	validate_tracking()	CSVs	dict	Track accuracy metrics
validation	sensitivity_analysis()	TIF + params	CSV, plots	Parameter sweep analysis
validation	generate_validation_report()	Results	PNG, CSV	Comprehensive validation summary

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Output Files

Step	Output Type	Example Files
Detection	PNG + MP4	Mito_Frame0.png, Mitochondria_Detection.mp4
Lysosome Count	PNG + CSV + MP4	Lyso_Count_Plot.png, Lysosome_Counts.csv
Morphology	PNG + MP4 + CSV	Morphology_Frame0_Labeled.png, Morphology_Labeled.mp4
Shape Features	PNG + CSV	Shape_Distributions.png, Mito_ShapeMetrics.csv
Shape Profiling	PNG + CSV	Shape_ViolinPlots.png, Combined_ShapeData.csv
Tracking	PNG + MP4 + CSV	Cumulative_Mito.png, Mito_Tracks.csv
Tracking Overlay	PNG + MP4	Cumulative_Composite.png, Composite_CumulativeTracks.mp4
Motility	PNG + CSV	Motility_Distributions.png, Motility_Scatter.png
Colocalization	PNG + MP4 + CSV	Colocalization_Frame0.png, Colocalization.csv
Summary	PNG + CSV	Integrated_CorrelationMatrix.png, Integrated_Merged_Data.csv
Validation	PNG + CSV	Validation_Report.png, Validation_Summary.csv

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Dependencies

Core Requirements:

• Python ≥ 3.9
• numpy ≥ 1.23.0
• pandas ≥ 1.5.0
• matplotlib ≥ 3.6.0
• seaborn ≥ 0.12.0
• opencv-python ≥ 4.6.0
• scikit-image ≥ 0.19.0
• scipy ≥ 1.9.0
• tifffile ≥ 2022.8.12

Optional:

• scikit-learn (for validation metrics)

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Visualization Design

Colorblind-Safe Palette:

• Mitochondria: #0173B2 (blue)
• Lysosomes: #DE8F05 (orange)

All visualizations use colorblind-safe colors to ensure accessibility and publication readiness.

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Statistical Notes

Correlation Analysis (Summary Module): The integrated summary module uses Spearman rank correlation to compute relationships between extracted metrics, providing a distribution-free assessment of feature associations.

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Comparison with Existing Tools

Feature	AutoMorphoTrack	CellProfiler	MiNA	MitoGraph	Imaris
Ease of Use	High (Python API)	Medium (GUI-heavy)	Medium	Medium	Low (proprietary)
Transparency	Full (open source)	Partial	Partial	Partial	Closed
Lightweight	Yes	No	No	No	No
Publication-Ready Outputs	Yes	Yes	Partial	Partial	Yes
Tracking	Yes	Optional	Yes	Yes	Yes
Shape Profiling	Yes	Limited	Yes	Yes	Yes
Colocalization	Yes	Yes	Limited	Limited	Yes
Customizable	Yes (modular)	Yes	Limited	Limited	No
Cost	Free	Free	Free	Free	Expensive

What Makes AutoMorphoTrack Different:

• Integrated workflow: All analyses in one package without external dependencies
• Full transparency: Complete source code and methodology documentation
• Lightweight: Minimal dependencies, fast execution
• Publication-ready: High-DPI outputs and colorblind-safe visualizations by default
• Modular design: Use only the modules you need

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AI Assistance Note

This package was developed with support from AI language models (e.g., ChatGPT, Claude). The term "AI assistance" refers to using external LLMs to help generate, review, and refine analysis code—NOT a built-in AI interface or algorithmic component. All algorithms are explicit, interpretable, and scientifically grounded.

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License

MIT License – See LICENSE.md for details.

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Citation

If you use this pipeline in your work, please cite:

@article{bayati2025automorphotrack,
  title={AutoMorphoTrack: Automated Organelle Tracking and Morphometric Profiling Toolkit},
  author={Bayati, Armin and others},
  year={2025}
}

Or in text:

Bayati, A. et al. AutoMorphoTrack: Automated Organelle Tracking and Morphometric Profiling Toolkit (2025)

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Documentation

For detailed documentation, examples, and tutorials, visit:

• GitHub: github.com/abayatibrain/AutoMorphoTrack
• Issues & Support: github.com/abayatibrain/AutoMorphoTrack/issues

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Author

Armin Bayati, Ph.D. Email: [e-mail]