oncologylab/histopia

Histology Spatial Topology for Omics Profiling and Inter-section Alignment

What it solves

Histopia addresses the complexity of analyzing serial-section histology and proteomic images. It provides tools to align multiple tissue sections, segment morphology across those sections, and reconstruct the tissue in 3D to understand spatial topology and omics profiling.

How it works

The software implements a multi-stage pipeline for tissue analysis:

  • Registration: Aligns serial sections using hybrid affine alignment, group-aware tissue masking, and quality control (QC) checks.
  • Semantic Atlas: Uses UNI2-h morphology regions to globally fit morphology and evaluate cross-section topology.
  • Semantic Topology: Creates tissue envelopes and semantic surfaces to build connected-volume reconstructions with explicit physical z-spacing.
  • Stain Profiling: Calculates relative optical density for various stains (such as H-DAB, Sirius Red, and PAS) with background correction.
  • Visualization: Provides an interactive 3D viewer for histology and stain stacks with linked ROI probes and native-resolution views.
  • QuPath Integration: Offers an extension to launch registration and semantic jobs directly from QuPath, using GeoJSON regions and native WSI coordinates.

Who it’s for

It is designed for computational researchers working with serial-section histology and proteomic imaging who need to reconstruct 3D tissue structures and perform quantitative stain analysis.

Highlights

  • Interactive 3D Reconstruction: Visualizes histology, semantic, and stain stacks in 3D.
  • Hardware Acceleration: Supports CPU, CUDA, and MPS for feature extraction.
  • Cachcing and Integrity: Uses checksummed caches and fingerprinting to ensure that changes in masks or geometry invalidate affected results.
  • QuPath Extension: Seamlessly integrates with the popular open-source bioimage analysis software QuPath.

Related

  • Project
    DigitalSlideArchive/HistomicsTK

    HistomicsTK is an open‑source Python toolkit for analyzing whole‑slide pathology images. It offers stain normalization, nuclei segmentation, and feature extraction, usable as a standalone library or as a plugin for the Digital Slide Archive web platform.

  • Project
    ContextLab/hypertools

    A Python toolbox for dimensionality reduction and visual exploration of high-dimensional data, enabling users to reduce and plot complex datasets in a single call.

  • Project
    qupath/qupath

    QuPath is open-source software for bioimage analysis that provides tools for annotating and analyzing whole slide and microscopy images using machine learning and specialized algorithms.

  • Project
    scverse/squidpy

    A scalable toolkit for the analysis and visualization of spatial molecular data and tissue microscopy images.

  • Project
    InsightSoftwareConsortium/ITK

    An open-source, cross-platform toolkit for N-dimensional scientific image processing, segmentation, and registration, primarily used for medical image analysis.