Lung Vasculature Analysis

Introduction

Features: Python, Pandas, Numpy, Matplotlib

This project was to analyze mice lung vasculature labeled with gold nanoparticles. 3D tomography was acquired from Zeiss Xradia. I used Amira to segment targeted regions by combining a series of background subtraction, edge detection, and manual labeling. Filament analysis was then applied by using built-in auto-skeletonization to extract the backbone of lung vasculature and its diameter.

Repository

• core:
  • filamentanalysis.py: label branches
  • fileop.py: io control
  • mkplot.py: generate plots
  • msxml.py: convert .xml files to .csv files
• test: test files
• archive_2018: previous codes
• par: lung_file_idx.csv: file information
• main.ipynb: main file for jupyter notebook
• main.py: main file for jupyter notebook in .py file. Support Vscode Python package
• README.md: readme file

Instruction

1. Clone the repository
2. Create a directory for data storage
3. Make a subfolder called 'raw' and copy the .xml files.
4. Copy par folder to the data folder
5. rename *.xml by adding index in the begin of the filename
   • for example: FOR7-DFF-Hypoxia.xml -> 17_FOR7-DFF-Hypoxia.xml. Index is based on lung_file_idx.csv
   • update the column of data_filename in lung_file_idx.csv
6. Run main.ipynb
7. Specify the directory of workspace
8. Run through the code and generate histograms:
   1. histo/length/*.png: frequency - length (µm)
   2. histo/thickness/*.png: frequency - thickness (µm)
   3. histo_summary/length.png: histogram in line plot style
   4. histo_summary/thickness.png: histogram in line plot style

License

This work is licensed under a Creative Commons Attribution 4.0 International License.