Unravelling the cytoskeletal architecture of cancer cells: a novel computational approach to predict cell invasive potential - CODE

This code is divided in 7 chapters:

Dataset: Inspect the dataset with RGB and 2D deconvoluted cytoskeleton/nuclei images.
Nuclei Preprocessing: Adjust the parameters for a given image to segment and preprocess nuclei and visualize the results.
Cytoskeleton Preprocessing: Adjust the parameters for a given image to indentify individual filaments and visualize the results.
Cell Segmentation: Use ROIpoly to draw a polygonal mask around the cell of interest.
Processing: Extract line segments and prepare cells for feature extraction.
Feature Extraction: Extract the desired features (DCFs, LSFs or CNFs).
Results Analysis: Compare feature values between single or a population of cells.

Additional notes:

Use the environment.yaml file to setup a dedicated environment with the required packages to run the code (TO DO).

Name		Name	Last commit message	Last commit date
Latest commit History 60 Commits
1 - Dataset.ipynb		1 - Dataset.ipynb
2 - Nuclei Preprocessing.ipynb		2 - Nuclei Preprocessing.ipynb
3 - Cytoskeleton PreProcessing.ipynb		3 - Cytoskeleton PreProcessing.ipynb
4 - Cell Segmentation.ipynb		4 - Cell Segmentation.ipynb
5 - Processing.ipynb		5 - Processing.ipynb
6 - Feature Extraction.ipynb		6 - Feature Extraction.ipynb
7 - Results Analysis.ipynb		7 - Results Analysis.ipynb
README.md		README.md
__init__.py		__init__.py