A Python library for scalable panoptic spatial analysis of histological WSIs

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 🧪

• Immuno-oncology Profiling:
  • Spatial Statistics of TILs.
  • Profiling TLS and Lymphoid Aggregates.
• Nuclear Pleomorphism:
  • Nuclear Morphology Analysis.
  • Nuclear Chromatin Distribution Analysis.
• TME Characterization:
  • Collagen Fiber Disorder Analysis.
  • Characterization of Desmoplastic Stroma.
• Nuclei Neighborhoods:
  • Tumor Cell Accessibility.

Installation 🛠️

pip install histolytics

Models 🤖

• Panoptic HoVer-Net
• Panoptic Cellpose
• Panoptic Stardist
• Panoptic CellVit-SAM
• Panoptic CPP-Net

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

@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}
}