Is it suitable for deep learning pipelines?

Absolutely, Histolab is specifically designed to prepare gigapixel images into standardized tiles for training and inference in computational pathology AI.

What are the different tiling strategies available?

You can use RandomTiler for diverse sampling, GridTiler for complete tissue coverage, or ScoreTiler to select the most informative regions based on nuclei density.

Can I filter out background or artifacts during extraction?

Yes, Histolab includes specialized masks like TissueMask and BiggestTissueBoxMask to isolate tissue regions and ignore background, artifacts, or pen marks.

How do I visualize the results before running a full extraction?

Histolab provides convenient methods like locate_mask() and locate_tiles() to preview processing results and tile coordinates on slide thumbnails.

What image formats does Histolab support?

Histolab supports common whole slide image (WSI) formats including SVS, TIFF, and NDPI, typically used in clinical and research pathology.

Histolab Pathology Processing

Name: Histolab Pathology Processing
Author: Zehong-Wang

byZehong-Wang

0•

数据科学与机器学习

Automates digital pathology workflows by processing whole slide images, detecting tissue regions, and extracting informative tiles for deep learning pipelines.

Histolab is a specialized toolkit designed for the complex requirements of digital pathology and computational medicine. It simplifies the handling of gigapixel whole slide images (WSI) in formats like SVS, TIFF, and NDPI, providing robust tools for automated tissue segmentation, background removal, and systematic tile extraction. By offering flexible strategies like random sampling, grid-based coverage, and quality-driven selection based on nuclei density, Histolab streamlines the transition from raw histological slides to high-quality datasets ready for machine learning model training and inference.

主要功能

01Integrated visualization for slides, masks, and tile locations

02Multi-format WSI support including SVS, TIFF, and NDPI via OpenSlide

03Automated tissue detection and sophisticated masking algorithms

04Flexible tile extraction strategies: Random, Grid, and Score-based

05Advanced image preprocessing filters and morphological operations

060 GitHub stars

使用场景

01Preparing large-scale histopathology datasets for deep learning model training

02Extracting high-information tiles based on cellularity or nuclei density scores

03Automating tissue segmentation and artifact removal in gigapixel images

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add zehong-wang/kosmos histolab

For use in Claude.ai and ChatGPT

Download Skill