Data

Tools

Indexes

Applications

Experiments

Open Source Science

https://github.com/hautaniemilab/histolytics

Interpretable Analysis of Histological WSIs

https://github.com/hautaniemilab/histolytics

Science Score: 36.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ✓
    Academic publication links
    Links to: arxiv.org, sciencedirect.com, nature.com
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (11.5%) to scientific vocabulary

Keywords

deep-learning digital-pathology panoptic-segmentation spatial-analysis wsi wsi-images
Last synced: 4 months ago · JSON representation

Repository

Interpretable Analysis of Histological WSIs

Basic Info
Statistics
  • Stars: 14
  • Watchers: 2
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Topics
deep-learning digital-pathology panoptic-segmentation spatial-analysis wsi wsi-images
Created 10 months ago · Last pushed 6 months ago
Metadata Files
Readme Changelog Contributing License

README.md

![Logo](imgs/histolytics_logo.png) **A Python library for scalable panoptic spatial analysis of histological WSIs** [![Github Test](https://img.shields.io/github/actions/workflow/status/HautaniemiLab/histolytics/tests.yml?label=tests)](https://github.com/HautaniemiLab/histolytics/blob/main/.github/workflows/tests.yml) [![License](https://img.shields.io/github/license/HautaniemiLab/histolytics)](https://github.com/HautaniemiLab/histolytics/blob/main/LICENSE) [![Python - Version](https://img.shields.io/pypi/pyversions/histolytics)](https://www.python.org/) [![Package - Version](https://img.shields.io/pypi/v/histolytics)](https://pypi.org/project/histolytics/) [![Model Checkpoints](https://img.shields.io/badge/%F0%9F%A4%97%20HF-Model%20Hub-yellow)](https://huggingface.co/histolytics-hub)

Introduction

histolytics is a spatial analysis library for histological whole slide images (WSI). Built upon torch, geopandas and libpysal, the library provides a comprehensive and scalable framework for panoptic segmentation and interpretable panoptic spatial analysis of routine histopathology slides.

Panoptic Segmentation Features 🌟

  • Fast WSI-level panoptic segmentation. See example.
  • Low memory-footprint segmentation results with __geo_interface__-specification.
  • Multiple vectorized segmentation output formats (geojson/feather/parquet).
  • Several panoptic segmentation model architectures for histological WSIs with flexible backbone support: See example
  • Pre-trained models in model-hub. See: histolytics-hub

Spatial Analysis Features 📊

  • Fast Spatial Querying of WSI-scale panoptic segmentation maps. See example
  • Spatial indexing/partitioning for localized spatial statistics and analysis. See example
  • Graph-based neighborhood analysis for local cell neighborhoods. See example
  • Plotting utilities for spatial data visualization. See example
  • Spatial clustering and cluster centrography metrics. See example
  • Large set of morphological, intensity, chromatin distribution, and textural features at nuclear level. See example
  • Large set of collagen fiber and intensity based features to characterize stroma and ECM. See example

Example Workflows 🧪

Installation 🛠️

shell pip install histolytics

Models 🤖

Contributing

We welcome contributions! To get started:

  1. Fork the repository and create your branch from main.
  2. Make your changes with clear commit messages.
  3. Ensure all tests pass and add new tests as needed.
  4. Submit a pull request describing your changes.

See contributing guide for detailed guidelines.

Citation

bibtex @article{2025histolytics, title={Histolytics: A Panoptic Spatial Analysis Framework for Interpretable Histopathology}, author={Oskari Lehtonen, Niko Nordlund, Shams Salloum, Ilkka Kalliala, Anni Virtanen, Sampsa Hautaniemi}, journal={XX}, volume={XX}, number={XX}, pages={XX}, year={2025}, publisher={XX} }

Owner

  • Name: Hautaniemi Lab
  • Login: HautaniemiLab
  • Kind: organization
  • Location: Finland

The Systems Biology of Drug Resistance in Cancer group, University of Helsinki

GitHub Events

Total
  • Create event: 5
  • Issues event: 3
  • Release event: 4
  • Watch event: 8
  • Delete event: 1
  • Issue comment event: 3
  • Public event: 1
  • Push event: 50
Last Year
  • Create event: 5
  • Issues event: 3
  • Release event: 4
  • Watch event: 8
  • Delete event: 1
  • Issue comment event: 3
  • Public event: 1
  • Push event: 50

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 147 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 5
  • Total maintainers: 1
pypi.org: histolytics

Panoptic Segmentation and WSI Spatial Analysis

  • Homepage: https://hautaniemilab.github.io/histolytics/
  • Documentation: https://hautaniemilab.github.io/histolytics/
  • License: BSD 3-Clause License Copyright (c) 2024-2025, HautaniemiLab All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  • Latest release: 0.2.2
    published 6 months ago
  • Versions: 5
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 147 Last month
Rankings
Dependent packages count: 9.0%
Average: 29.9%
Dependent repos count: 50.8%
Maintainers (1)
okunator
Last synced: 5 months ago

Dependencies

.github/workflows/docs.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v5 composite
  • astral-sh/setup-uv v5 composite
.github/workflows/tests.yml actions
  • actions/checkout v3 composite
  • actions/checkout v4 composite
  • actions/setup-python v5 composite
  • astral-sh/setup-uv v5 composite
pyproject.toml pypi
  • albumentations >=2.0.6
  • cellseg-models-pytorch >=0.1.26
.github/workflows/publish.yml actions
  • actions/checkout v4 composite
  • astral-sh/setup-uv v3 composite