| Graphs | |
| ========= | |
| ``PathML`` provides a Graph API to construct cell or tissue graphs from Whole-Slide Images (WSIs). | |
| .. note:: | |
| Graphs are a data structure comprised of nodes connected by edges, which allow for explicit modeling of spatial relationships. | |
| In computational pathology, nodes may represent tissue regions or individual nuclei, and the resulting graph structure can be | |
| used to study the spatial organization of the specimen. | |
| We provide template code below for cell graph construction. | |
| .. code-block:: | |
| # load packages | |
| from pathml.core import HESlide | |
| from pathml.preprocessing import Pipeline, NucleusDetectionHE | |
| from pathml.graph import KNNGraphBuilder | |
| from pathml.graph.utils import get_full_instance_map | |
| # Define slide path | |
| slide_path = 'PATH TO SLIDE' | |
| # Initialize pathml.core.slide_data.HESlide object | |
| wsi = HESlide(slide_path, name = slide_path, backend = "openslide", stain = 'HE') | |
| # Set up PathML pipeline for nuclei detection | |
| pipeline = Pipeline([NucleusDetectionHE(mask_name = "detect_nuclei")]) | |
| # Run pipeline to get nuclei segmentation masks | |
| wsi.run(pipeline, overwrite_existing_tiles=True, distributed=False, tile_pad=True, tile_size=PATCH_SIZE) | |
| # Extract the nuclei segmentation masks | |
| image, nuclei_map, nuclei_centroid = get_full_instance_map(wsi, patch_size = PATCH_SIZE, mask_name="detect_nuclei") | |
| # Initialize a pathml.graph.KNNGraphBuilder object | |
| knn_graph_builder = KNNGraphBuilder(k=5, thresh=50, add_loc_feats=True) | |
| # Build the cell graph | |
| cell_graph = knn_graph_builder.process(nuclei_map, return_networkx=True) | |
| For a full example that considers tissue graph construction and feature extraction for machine learning, please refer to the Graph construction and processing tab under Examples. |