Can I filter tiles based on tissue content?

Yes, Histolab includes functionality to check tissue percentage, allowing you to automatically discard tiles that contain mostly background or white space.

Does it handle very large images efficiently?

Yes, it utilizes pyramidal image structures to access specific levels and regions, ensuring high performance without requiring massive amounts of RAM for gigapixel files.

What slide formats does Histolab support?

Histolab supports major gigapixel pathology formats including SVS, TIFF, and NDPI by leveraging the OpenSlide backend for efficient image access.

What tiling strategies are available in this skill?

You can utilize RandomTiler for diverse sampling, GridTiler for systematic coverage, or ScoreTiler to prioritize tiles with high biological significance like nuclei density.

Is this skill suitable for deep learning dataset preparation?

Absolutely. It is specifically designed to bridge the gap between raw whole slide images and the tiled datasets required for training histopathology neural networks.

Histolab Digital Pathology

Name: Histolab Digital Pathology
Author: x-cmd

byx-cmd

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数据科学与机器学习

Processes gigapixel whole slide images for digital pathology, automating tissue detection and tile extraction for deep learning pipelines.

Histolab is a specialized toolkit designed to streamline the complex workflow of digital pathology image analysis within AI-assisted coding environments. It enables developers and researchers to handle gigapixel whole slide images (WSI) in various formats like SVS and NDPI, providing automated tools for tissue segmentation, artifact removal, and informative tile extraction. By offering sophisticated tiling strategies—including random, grid, and score-based sampling—Histolab simplifies the preparation of high-quality datasets for computational pathology and deep learning models, ensuring that only relevant tissue regions are utilized for training and inference.

主要功能

01Support for diverse gigapixel WSI formats such as SVS, TIFF, and NDPI.

02Comprehensive visualization tools for slide thumbnails, mask verification, and tile reporting.

038 GitHub stars

04Automated tissue detection and binary mask generation to exclude background artifacts.

05Advanced image preprocessing filters for color space conversion and morphological operations.

06Multi-strategy tiling including Random, Grid, and Nuclei-based Score extraction.

使用场景

01Extracting high-resolution tiles from large tissue slides for training histological CNN models.

02Ranking and selecting the most informative regions of a slide based on cellularity and nuclei density.

03Automating tissue segmentation and background removal in large-scale pathology datasets.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add x-cmd/skill histolab

For use in Claude.ai and ChatGPT

Download Skill