histolab

Automate whole-slide image tile extraction and preprocessing with tissue masking.

94|11|Updated Mar 26, 2026
One-click install
npx skills add https://github.com/swaruplab/operon --skill histolab-swaruplab
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: histolab
Source: https://github.com/swaruplab/operon/tree/main/src-tauri/protocols/histolab
Command: npx skills add https://github.com/swaruplab/operon --skill histolab-swaruplab

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Automates the end-to-end processing of whole-slide images, enabling researchers and clinicians to quickly generate tissue-focused tiles for downstream analysis without manual preprocessing.

Core Features & Use Cases

  • Tile extraction using RandomTiler, GridTiler, and ScoreTiler on whole-slide images.
  • Tissue detection and masking with predefined masks (TissueMask, BiggestTissueBoxMask) to filter tiles.
  • Preprocessing pipelines using image filters and morphological operations to enhance tissue features.
  • Reference-driven workflows and documentation stored under references/ to guide usage and troubleshooting.

Quick Start

Load a slide and run the default tiling workflow to extract tissue tiles and generate preprocessing outputs.

Frequently Asked Questions about histolab

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I extract tiles from whole-slide images for digital pathology workflows?

You can extract tiles from whole-slide images using RandomTiler, GridTiler, or ScoreTiler. These methods automate tissue-focused tile generation by applying tissue masks to filter relevant regions for downstream analysis.

What is tissue masking and how does it filter tiles during slide preprocessing?

Tissue masking uses predefined masks like TissueMask or BiggestTissueBoxMask to identify tissue regions. It filters extracted tiles by retaining only those covering actual tissue areas, discarding empty background.

Does histolab support both clinical and research contexts for whole-slide image preprocessing?

Yes, histolab supports both research and clinical contexts. It applies to digital pathology workflows requiring tissue masking, tile sampling, and preprocessing pipelines across these environments.

Can I apply image filters and morphological operations to enhance tissue features after tile extraction?

Yes, you can apply preprocessing pipelines using image filters and morphological operations. These enhance tissue features within the extracted tiles for subsequent analysis.

What's the best way to sample tiles across an entire whole-slide image without manual preprocessing?

The best way to sample tiles is using end-to-end tiling with RandomTiler, GridTiler, or ScoreTiler. These automate tissue-focused tile sampling across whole-slide images, eliminating manual preprocessing.

Where can I find reference documentation for troubleshooting whole-slide image tile extraction workflows?

Reference documentation is stored under the references directory. It provides workflow guidance and troubleshooting for tile extraction and preprocessing of whole-slide images.