Histolytics

Histolytics integrates panoptic segmentation with spatial querying, morphological profiling, and graph-based analytics to enable high-resolution, quantitative characterization of nuclei, tissue compartments, and the extracellular matrix in whole-slide images.

Key Features:

• Panoptic Segmentation: Supports segmentation with state-of-the-art deep learning models for nuclei, tissue compartments, and extracellular matrix across entire whole-slide images.
• Spatial Querying: Enables spatially contextualized measurements at cellular and tissue levels through spatial querying capabilities.
• Morphological Profiling: Extracts biologically grounded morphological features across entire whole-slide images.
• Graph-based Analytics: Incorporates graph-based analytics for quantitative characterization of tissue organization.
• Scalable Framework: Provides a scalable Python framework for interpretable, whole-slide image-scale histopathological analysis.

Scientific Applications:

• Computational Pathology: Offers an interpretable alternative or complement to black-box predictive models in explainable computational pathology.
• Tissue Pattern Analysis: Uncovers tissue-level patterns relevant to pathology through quantitative spatial organization analysis in hematoxylin and eosin-stained whole-slide images.
• Cancer Research: Validated through segmentation benchmarking on cervical and ovarian high-grade serous carcinoma data.

Methodology:

Histolytics integrates panoptic segmentation with spatial querying, morphological profiling, and graph-based analytics. Segmentation is performed using state-of-the-art deep learning models. The framework was validated through segmentation benchmarking on cervical and ovarian high-grade serous carcinoma data.