PyPathomics

PyPathomics is an open-source software for gigapixel whole-slide image analysis. Off-the-shelf and easy-to-use.

Currently under development. This is a simplified version of [sc_MTOP] [Paper]

Support for:

• Hover-Net [Repo] [Paper] (Graham et al., 2019)

• Cerberus (In Beta) [Repo] [Paper] (Graham et al., 2023)

How it works?

Get all cell and slide-level features directly through the cell segmentation file like Hover-Net's .json file.

Installation

conda create -n pypathomics
conda activate pypathomics
pip install -r requirements.txt

Options and Usage

Options:

Options for config.yaml

   openslide-home: path\to\openslide-home # for Windows only
   feature-set: ['Morph', 'Texture', 'Triangle']
   cell_types: ['I', 'S', 'T']
   statistic-types: ['basic', 'distribution']

Where,

• openslide-home: Specifies the path to the OpenSlide library. (for Windows only, leave it empty for Linux)
• feature-set: A list of feature sets to extract. Options: Morph, Texture, Triangle.
• cell_types: Types of cells to analyze. Options: I (inflammatory), S, (stromal), T (tumor).
• statistic-types: Types of statistics to calculate. Options: basic, distribution.

statistics-types	e.g.
basic	mean, std
distribution	Q25, Q75, median, IQR, range, skewness, kurtosis

Options for main.py

Required Arguments:

    --config     Specify the configuration file path
    --seg        Path to the segmentation directory or file from Hover-Net(.json) or Cerberus(.dat)
    --wsi        Path to the WSI directory or file
    --ext        WSI file extension (default: .svs)
    --buffer     Specify the output buffer dir for preprocessing
    --output     Set the output directory for the analysis

Optional Arguments:

    -f           Run for a single file (default: run for directory) 
    --auto_skip  Skip existing directories automatically (default: True)
    --level      Detail level of the WSI to analyze (default: 0)

Usage:

1. Make sure you run the Hover-Net's wsi seg or Cerberus' wsi seg and get the seg files.

2. Modify and check the config.yaml before running.

3. Analyze a Directory:

   python main.py --seg /path/to/seg_dir --wsi /path/to/wsi_dir --buffer /path/to/buffer --ext .svs --output /path/to/output.csv

4. Analyze a Single File

   python main.py -f --seg /path/to/seg_file --wsi /path/to/wsi_file --buffer /path/to/buffer --ext .svs --output /path/to/output.csv

All-cell Info, stored in /path/to/buffer

Feature	Description
Name	Identifier for the cell
Centroid	Position of the cell's centroid
Cell Type	Information about the cell type

Slide Cell Ratio

Feature	Description
Ratio	Reflects the proportion of cells of this type

Slide Morphological Features

Feature	Description
Area	Area of the cell, indicating cell size
AreaBbox	Area of the minimum bounding rectangle around the cell
CellEccentricities	Eccentricity of the cell
Circularity	Roundness of the cell
Elongation	Elongation rate of the cell
Extent	Proportion of the cell occupying its bounding rectangle
MajorAxisLength / Morph_MinorAxisLength	Lengths of the major and minor axes of the fitted ellipse for the cell
Perimeter	Perimeter of the cell boundary
Solidity	Ratio of the cell area to its convex hull area
CurvMean / Std / Max / Min	Mean, standard deviation, maximum, and minimum of the cell boundary curvature

Texture Features

Feature	Description
ASM (Angular Second Moment)	Texture consistency, measuring the similarity between a pixel and its neighbors
Contrast	Texture contrast, describing the intensity variation in the image
Correlation	Texture correlation, measuring the similarity between a pixel and its neighbors
Entropy	Texture entropy, representing the diversity of information in the image; higher values indicate more complex textures
Homogeneity	Texture homogeneity, assessing the consistency of the texture
IntensityMean / Std / Max / Min	Mean, standard deviation, maximum, and minimum of the texture intensity

Delaunay Triangle Spatial Features

Feature	Description
Area	Area of the Delaunay triangle around the cell
Perimeter	Perimeter of the Delaunay triangle around the cell
Angle_Range	Difference between the maximum and minimum angles of the Delaunay triangle around the cell

Citation

  @software{pypathomics,
    author       = {HY Cui and XX Wang and J Xu and DP Chen},
    title        = {PyPathomics},
    year         = 2024,
    publisher    = {GitHub},
    url          = {https:/HaoyuCui/PyPathomics},
    version      = {1.0},
  }