Standalone pythomics feature extraction

Extract per-object lumen and epithelium measurements and block-summed density maps from an integer label-map image. This folder is self-contained and does not import the repository's model/inference code.

Install

From this directory, install the listed dependencies into your environment:

python -m pip install -r requirements.txt

Image readers are provided by scikit-image. Parquet output is deliberately not required; object-level tables are CSV, density arrays are compressed NPZ, and MAT output is optional.

CLI

Run from the repository root:

python -m pythomics_standalone path/to/labels.png --output-dir output --save-mat

The default label values are lumen=1, epithelium=2, stroma=4, epithelial-cells=5, stromal-cells=6. Override with --lumen-label, --epithelium-label, --stroma-label, --epithelial-cells-label, and --stromal-cells-label. --min-area sets the minimum connected component size in source pixels (default 2048); --block-size HEIGHT WIDTH sets density aggregation blocks (default 20 20).

Epithelium regions include both epithelium and epithelial-cell pixels. Stroma density includes stromal-cell pixels. Connected components are labeled independently in each tissue mask. Tables contain source-pixel area, circularity (roundness), and for epithelium average skeleton thickness and epithelial-cell fraction. Density NPZ arrays are block sums, with edge blocks padded with zeros. The optional MAT file includes densities plus feature maps projected across every source-resolution pixel.

Outputs are <input-stem>_lumen.parquet, <input-stem>_epithelium.parquet, and <input-stem>_densities.npz; with --save-mat, also <input-stem>_features.mat at density (block) resolution, or full resolution with --save-full-mat. To build a MAT file from an existing label map and tables, pass --lumen-parquet and --epithelium-parquet. -j/--jobs sets worker processes (default: up to 12 CPUs); specify a higher value to use more.