automorphotrack 1.0.5

AutoMorphoTrack is an open-source Python package for automated detection, morphology classification, motility tracking, and colocalization analysis of organelles in multichannel fluorescence microscopy data.

AutoMorphoTrack

AutoMorphoTrack is an open-source Python package for automated detection, morphology classification, shape profiling, motility tracking, and colocalization analysis of subcellular organelles in multichannel fluorescence microscopy data.

This package represents the fully modular version of the AutoMorphoTrack pipeline, originally developed as a Jupyter Notebook by Armin Bayati, Ph.D., and now distributed as an installable Python framework.
Repository: https://github.com/abayatibrain/automorphotrack

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🧬 Overview

AutoMorphoTrack automates quantitative analysis of time-lapse .tif image stacks—typically dual-channel recordings of mitochondria and lysosomes—and generates publication-ready visual and numerical outputs at every step of the analysis pipeline.

Pipeline stages:

1. Detection – Organelle segmentation and outline visualization
2. Lysosomal Counting – Per-frame lysosome enumeration and temporal profiling
3. Morphology Classification – Elongated vs. punctate mitochondria
4. Shape Feature Extraction – Area, circularity, solidity, aspect ratio, orientation
5. Tracking – Cumulative organelle trajectories (mitochondria, lysosomes, composite)
6. Motility Analysis – Velocity, displacement, and trajectory-based statistics
7. Colocalization – Bright-blue overlap overlays and correlation metrics
8. Integrated Summary – Correlation matrices linking morphology, motility, and overlap

Each stage outputs high-resolution figures, videos, and CSV data tables stored in standardized subdirectories, ensuring reproducibility and compatibility with any image dataset.

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📦 Installation

Install directly from PyPI:

pip install AutoMorphoTrack

Or from source:

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

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🚀 Basic Usage

from automorphotrack import *

tif_path = "Composite.tif"

detect_organelles(tif_path)
count_lysosomes_per_frame(tif_path)
classify_morphology(tif_path)
analyze_shape_features(tif_path)
track_organelles(tif_path)
analyze_motility()
analyze_colocalization(tif_path)
summarize_integrated_data()

Each function automatically generates visual and quantitative outputs at every step.

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📁 Outputs

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
Tracking	PNG + MP4 + CSV	Cumulative_Mito.png, Mito_Tracks.csv
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

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🔧 Dependencies

• Python ≥ 3.9
• numpy, pandas, matplotlib, seaborn, opencv-python, scikit-image, scipy, tifffile

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🧩 Citation

If you use AutoMorphoTrack in your research, please cite:

Bayati, A. (2025). AutoMorphoTrack: An accessible Python framework for automated analysis of organelle morphology and motility.
GitHub Repository: https://github.com/abayatibrain/automorphotrack