Histolytics: A panoptic spatial analysis framework for interpretable histopathology.

Area of Science:

• Computational pathology
• Digital pathology
• Bioinformatics

Background:

• Hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) are crucial for pathological analysis.
• Quantifying spatial organization within WSIs reveals tissue-level patterns relevant to disease.
• Current methods may lack interpretability or scalability for WSI-scale analysis.

Purpose of the Study:

• To introduce Histolytics, an open-source Python framework for interpretable, WSI-scale histopathological analysis.
• To enable high-resolution, quantitative characterization of cellular and tissue components within WSIs.
• To provide an interpretable alternative or complement to black-box predictive models in computational pathology.

Main Methods:

• Integration of panoptic segmentation with spatial querying, morphological profiling, and graph-based analytics.
• Utilizing state-of-the-art deep learning models for segmentation of nuclei, tissue compartments, and extracellular matrix (ECM).
• Development of modular tools for extracting biologically grounded features across entire WSIs.

Main Results:

• Histolytics enables scalable and interpretable analysis of H&E-stained WSIs.
• The framework facilitates high-resolution characterization of tissue spatial organization.
• Validation through segmentation benchmarking on cervical and ovarian high-grade serous carcinoma data.

Conclusions:

• Histolytics addresses a critical gap in explainable computational pathology.
• The framework provides spatially contextualized measurements for deeper biological insights.
• Histolytics supports diagnostic reasoning by offering interpretable computational pathology tools.