Histolab Pathology Toolkit

About

Histolab is a specialized Claude Code Skill designed for computational pathology researchers and developers working with massive whole slide images (WSI). It streamlines the complex process of handling gigapixel medical data by providing automated tissue detection, sophisticated mask generation, and diverse tile extraction strategies. Supporting formats like SVS, TIFF, and NDPI, this skill bridges the gap between raw histological slides and production-ready deep learning pipelines, enabling users to efficiently sample informative regions while filtering out artifacts and background noise.

Key Features

• Support for major gigapixel WSI formats such as SVS, TIFF, and NDPI
• Advanced image filtering for tissue enhancement and pen annotation removal
• Integrated visualization tools for slide thumbnails and tile distribution analysis
• Multi-strategy tile extraction including Random, Grid, and Score-based tiling
• 81 GitHub stars
• Automated tissue detection and sophisticated binary mask generation

Use Cases

• Performing spatial analysis and systematic grid-based slide scanning
• Preparing large-scale datasets for histopathology deep learning training
• Automating tissue region segmentation and background exclusion for research