Processes and prepares whole slide pathology images for deep learning and digital pathology workflows.
Histolab is a specialized digital pathology skill that simplifies the complex task of handling gigapixel Whole Slide Images (WSI). It provides robust tools for automated tissue detection, background/artifact exclusion, and extracting meaningful tiles through various strategies like random sampling, systematic grid coverage, or score-based selection based on nuclei density. By standardizing the preprocessing of large-scale formats like SVS and TIFF, this skill enables researchers and developers to efficiently build high-quality datasets for computational pathology and histological analysis within the Claude environment.
Key Features
01Comprehensive support for gigapixel WSI formats such as SVS, TIFF, and NDPI
02Integrated visualization for slide thumbnails, masks, and tile locations
03Advanced image preprocessing for pen removal and nuclei enhancement
041 GitHub stars
05Multi-strategy tile extraction including Grid, Random, and Score-based methods
06Automated tissue detection and mask generation using advanced filtering
Use Cases
01Preparing histological datasets for training deep learning models
02Performing systematic spatial analysis across whole tissue sections
03Identifying and extracting high-cellularity regions for diagnostic research
