API Reference

Welcome to the Histolytics API Reference. Here you'll find an overview of all public objects, functions and methods implemented in Histolytics.

Modules

Data

Sample datasets

• cervix_nuclei: A GeoDataframe of segmented nuclei of a cervical biopsy.
• cervix_tissue: A GeoDataframe of segmented tissue regions of a cervical biopsy.
• cervix_nuclei_crop: A GeoDataframe of segmented nuclei of a cervical biopsy (cropped).
• cervix_tissue_crop: A GeoDataframe of segmented tissue regions of a cervical biopsy (cropped).
• hgsc_nuclei_wsi: A GeoDataframe of segmented nuclei of a HGSC whole slide image.
• hgsc_tissue_wsi: A GeoDataframe of segmented tissue regions of a HGSC whole slide image.
• hgsc_cancer_nuclei: A GeoDataframe of segmented nuclei of a HGSC tumor nest.
• hgsc_cancer_he: A 1500x1500 H&E image of HGSC containing a tumor nest.
• hgsc_stroma_nuclei: A GeoDataframe of segmented nuclei of a HGSC stroma.
• hgsc_stroma_he: A 1500x1500 H&E image of HGSC containing stroma.

Losses

Loss functions for panoptic segmentation

• BCELoss: Binary Cross Entropy Loss.
• CELoss: Cross Entropy Loss.
• DiceLoss: Dice Loss.
• FocalLoss: Focal Loss.
• JointLoss: Joint Loss. Combines arbitrary number of losses into one.
• MSE: Mean Squared Error Loss.
• MAE: Mean Absolute Error Loss.
• MultiTaskLoss: Multi-task loss for panoptic segmentation. Combines multiple losses for multi prediction tasks like panoptic segmentation.
• SSIM: Structural Similarity Index Loss.
• TverskyLoss: Tversky Loss.

Metrics

Metrics for panoptic segmentation

• accuracy_multiclass: Accuracy metric for multiclass segmentation.
• aggregated_jaccard_index: Aggregated Jaccard Index for multiclass segmentation.
• average_precision: Average Precision metric for multiclass segmentation.
• dice_multiclass: Dice metric for multiclass segmentation.
• dice2: Alternative Dice metric for multiclass segmentation.
• f1score_multiclass: F1 score metric for multiclass segmentation.
• iou_multiclass: Intersection over Union (IoU) metric for multiclass segmentation.
• pairwise_object_stats: Pairwise object statistics (TP, FP, TN, FN) for instance segmentation.
• pairwise_pixel_stats: Pairwise pixel-level statistics (TP, FP, TN, FN) for instance segmentation.
• panoptic_quality: Panoptic Quality metric for panoptic segmentation.
• sensitivity_multiclass: Sensitivity metric for multiclass segmentation.
• specificity_multiclass: Specificity metric for multiclass segmentation.

Models

Panoptic segmentation models

• CellposePanoptic: Panoptic segmentation model based on Cellpose.
• CellVitPanoptic: Panoptic segmentation model based on CellVit.
• CPPNetPanoptic: Panoptic segmentation model based on CPPNet.
• HoverNetPanoptic: Panoptic segmentation model based on HoverNet.
• StarDistPanoptic: Panoptic segmentation model based on StarDist.

Nuclei Features

Extracting features from nuclei

• chromatin_clumps: Extract chromatin clumps from a nuclei segmentation.
• grayscale_intensity: Extract grayscale intensity features from a nuclei segmentation.
• rgb_intensity: Extract RGB intensity features from a nuclei segmentation.

Spatial Aggregation

Neighborhood statistics and grid aggregation

• local_character: Get summary metrics of neighboring nuclei features.
• local_diversity: Get diversity indices of neighboring nuclei features.
• local_distances: Get distances to neighboring nuclei.
• local_vals: Get local values of neighboring nuclei.
• local_type_counts: Get counts of neighboring nuclei types.
• grid_agg: Aggregate spatial data within grid cells.

Spatial Clustering

Clustering and cluster metrics

• density_clustering: Perform density-based clustering on spatial data.
• lisa_clustering: Perform Local Indicators of Spatial Association (LISA) clustering.
• cluster_feats: Extract features from spatial clusters.
• cluster_tendency: Calculate cluster tendency (centroid).
• local_autocorr: Calculate local Moran's I for each object in a GeoDataFrame.
• global_autocorr: Calculate global Moran's I for a GeoDataFrame.
• ripley_test: Perform Ripley's alphabet analysis for GeoDataFrames.

Spatial Geometry

Morphometrics and shapes

• shape_metric: Calculate shape moprhometrics for polygon geometries.
• line_metric: Calculate shape moprhometrics for line geometries.
• medial_lines: Create medial lines of input polygons.
• hull: Create various hull types around point sets.

Spatial Graph

Graph fitting

• fit_graph: Fit a graph to a GeoDataFrame of segmented objects.
• get_connected_components: Get connected components of a spatial graph.
• weights2gdf: Convert spatial weights to a GeoDataFrame.

Spatial Operations

Spatial querying and partitioning

• get_objs: Query segmented objects from specified regions.
• get_interfaces: Get interfaces of two segmented tissues.
• rect_grid: Partition a GeoDataFrame into a rectangular grid.
• h3_grid: Partition a GeoDataFrame into an H3 hexagonal spatial index (grid).
• quadbin_grid: Partition a GeoDataFrame into a Quadbin spatial index (grid).

Stroma Features

Extracting features from stroma

• extract_collagen_fibers: Extract collagen fibers from a H&E images.
• stromal_intensity_features: Compute intensity features from a H&E image representing stroma.
• get_hematoxylin_mask: Get hematoxylin mask from a H&E image.
• get_eosin_mask: Get eosin mask from a H&E image.
• tissue_components: Extract background, foreground, and nuclear components from a H&E image.
• kmeans_img: Perform KMeans clustering on an image.
• hed_decompose: Transform an image to HED space.

Transforms

Image and instance label transforms for model training

• AlbuStrongAugment: Apply StrongAugment augmentation algorithm.
• ApplyEach: Apply a functions to label masks and return each output separately.
• BinarizeTransform: Binarize label masks.
• CellposeTransform: Transform label masks to Cellpose flow maps.
• ContourTransform: Transform label masks to contour maps.
• DistTransform: Transform label masks to distance maps.
• EdgeWeightTransform: Transform label masks to edge weight maps.
• HoverNetTransform: Transform label masks to HoverNet horizontal and vertical gradient maps.
• MinMaxNormalization: Apply Min-Max normalization to input image.
• Normalization: Normalize/Standardize input image.
• PercentileNormalization: Normalize input image using percentiles.
• SmoothDistTransform: Transform label masks to smooth distance maps.
• StarDistTransform: Transform label masks to StarDist star-distance maps.

Utils

Utility functions and classes

gdf

• gdf_apply: Apply a function to a GeoDataFrame in parallel.
• gdf_to_polars: Convert a GeoDataFrame to a Polars DataFrame.
• get_centroid_numpy: Get the centroids of a GeoDataFrame as a NumPy array.
• set_uid: Set a unique identifier (UID) for each object in a GeoDataFrame.
• set_geom_precision: Set the precision of geometries in a GeoDataFrame.

raster

• inst2gdf: Convert an instance segmentation mask to a GeoDataFrame.
• sem2gdf: Convert a semantic tissue segmentation mask to a GeoDataFrame.
• gdf2inst: Convert a GeoDataFrame to an instance segmentation mask.
• gdf2sem: Convert a GeoDataFrame to a semantic tissue segmentation mask.

plot

• draw_thing_contours: Draw contours of segmented nuclei and overlay them on an image.
• legendgram: Create a histogram legend for a specified column in a GeoDataFrame.

WSI (Whole Slide Images)

WSI handling and WSI-level segmentation

• SlideReader: Functions for reading whole slide images
• WsiPanopticSegmenter: Class handling the panoptic segmentation of whole slide images
• get_sub_grids: Get sub-grids from a whole slide image.