morphomeasure 0.1.3

Python package and CLI for L-Measure-based morphometric extraction

MorphoMeasure

A Python package and command-line tool for automated extraction and summarization of morphometric features from neuron morphology files (SWC format) using L-Measure. It supports batch processing, flexible feature selection, tag-based extraction, and outputs results in convenient CSV formats for downstream analysis. This package also supports the extraction of ABEL, a recently discovered morphometric feature.

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Quick Install

From PyPI (recommended):

pip install morphomeasure

Or for development:

git clone https://github.com/Masood-Akram/MorphoMeasure.git
cd MorphoMeasure
pip install -e .

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Features

• Python package & CLI for automated neuronal morphometric extraction using L-Measure
• Batch processing of many SWC files
• Outputs both branch-by-branch and summary morphometrics
• Handles dendritic (3.0, 4.0) and glial (7.0) tags
• Requires no Java or L-Measure installation - Lm.exe is bundled

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Requirements

• Python 3.8+
• L-Measure (Lm.exe) is bundled with the package
• pandas (auto-installed)

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Usage

Python API

from morphomeasure import LMeasureWrapper

lm = LMeasureWrapper()
lm.run_batch(
    swc_dir='/path/to/swc-directory',
    output_dir='/path/to/output-directory',
    tags=['3.0'], # can be ['3.0', '4.0'] for apical & basal, and ['7.0'] for glia
    features_mode=['all']   # can be ['all', 'branch'], or ['all', 'branch', 'combined']
)

As a CLI Tool

morphomeasure --tag 3.0 4.0 --features all --swc_dir /path/to/swc_files --output_dir /path/to/output

All options:

morphomeasure --help

Arguments:

Argument	Description	Example
--tag	Tags to process (e.g., 3.0 for basal, 4.0 for apical, 7.0 for glia)	--tag 3.0 4.0
--features	Output type: all, branch, or combined	--features all
--swc_dir	Directory containing input SWC files	--swc_dir ./swc_files
--output_dir	Directory to save output CSVs	--output_dir ./Measurements
--tmp_dir	Temporary directory for intermediate files (default: ./tmp)	--tmp_dir ./tmp
--lm_exe_path	Path to L-Measure executable (default: bundled with package)	--lm_exe_path ./Lm/Lm.exe

Output

• Complete structure CSVs:
  Measurements/<tag_label>/All_Morphemetrics.csv

• Apical & Basal CSVs:
  Measurements/<tag_label>/All_Morphemetrics_apical.csv Measurements/<tag_label>/All_Morphemetrics_basal.csv

• Branch-by-branch CSVs:
  Measurements/<tag_label>/Branch_Morphometrics_<neuron>.csv

• Summary CSVs:

  • All_Morphometrics.csv (overall structure)
  • All_Morphometrics_basal.csv (basal dendrites)
  • All_Morphometrics_apical.csv (apical dendrites)
  • All_Morphometrics_glia.csv (glial processes)

• Temporary files:
  Cleaned up automatically from the tmp directory.

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Customization

• Features:
  Edit morphomeasure/features.py to add or remove L-Measure features.
• Summary Logic:
  Update the summary_logic dictionary in features.py.
• Tag Labels:
  Edit TAG_LABELS in features.py.

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Notes

• Temporary CSVs are cleaned up from tmp/ after processing.
• Make sure Lm.exe is compatible with your OS (bundled version is for Windows).
• For L-Measure feature details, see the L-Measure documentation.

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Roadmap

• Extract features with tag 3 (basal) and tag 4 (apical) together and separately.
• Extract features with tag 7 (glia).
• PCA on Height, Width, and Depth.
• Pure Python feature extraction (no L-Measure dependency).
• Separate measures for apical & basal trees.

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License

This project is licensed under the MIT License. See the LICENSE file for details.

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Citation

If you use this repository in your research, please cite the associated papers:

@article{https://doi.org/10.1038/nprot.2008.51,
  author = {Scorcioni, R., Polavaram, S., & Ascoli, G. A.},
  doi = {https://doi.org/10.1038/nprot.2008.51},
  journal = {Nature protocols},
  number = {5},
  pages = {866–-876},
  title = {{L-Measure: a web-accessible tool for the analysis, comparison and search of digital reconstructions of neuronal morphologies}},
  volume = {3},
  year = {2008}
}

@article{https://doi.org/10.1002/jnr.25131,
  author = {Akram, M. A., Wei, Q., & Ascoli, G. A.},
  doi = {https://doi.org/10.1002/jnr.25131},
  journal = {Journal of Neuroscience Research},
  number = {1},
  pages = {112--129},
  title = {{Machine learning classification reveals robust morphometric biomarker of glial and neuronal arbors}},
  volume = {101},
  year = {2023}
}

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Acknowledgments

• L-Measure: Center for Neural Informatics, Neural Structures, & Neural Plasticity, George Mason University

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For questions or contributions, please open an issue or pull request!