Histolab Digital Pathology

About

Histolab is a specialized toolkit designed to handle the complexities of gigapixel digital pathology images. It provides high-level abstractions for loading various Whole Slide Image (WSI) formats like SVS and NDPI, automatically segmenting tissue from background, and extracting informative tiles for downstream analysis. Whether you're performing exploratory sampling with random tiling, ensuring complete coverage with grid-based extraction, or prioritizing high-cellularity regions via score-based selection, Histolab streamlines the bridge between raw pathology slides and production-grade deep learning datasets.

Key Features

• Automated tissue segmentation and background filtering using sophisticated image masks.
• Advanced visualization tools for previewing masks, tile locations, and quality reports.
• Flexible tile extraction strategies including Random, Grid, and Score-based tiling.
• 16 GitHub stars
• Comprehensive WSI support for industry-standard formats like SVS, TIFF, and NDPI.
• Integrated image processing pipeline for color space conversion, morphology, and artifact removal.

Use Cases

• Preprocessing gigapixel pathology slides for training and evaluating deep learning models.
• Identifying and extracting high-information regions of interest based on nuclei density or cellularity.
• Systematic spatial analysis of tissue sections through automated grid-based tile extraction.