How does Histolab help with machine learning data preparation?

It automates the tedious task of breaking down gigapixel slides into smaller, manageable tiles and uses scoring functions to ensure only the most informative tissue samples are used for training.

What image formats does the Histolab skill support?

Histolab supports common Whole Slide Image (WSI) formats used in pathology, including SVS, TIFF, and NDPI, leveraging pyramidal image structures for efficient access.

Can I filter out artifacts like pen marks or background from slides?

Yes, Histolab provides specialized masks and filters, such as TissueMask and custom morphological pipelines, specifically designed to isolate tissue and remove non-informative artifacts.

What is the benefit of using the ScoreTiler?

The ScoreTiler ranks tiles based on specific biological features, such as nuclei density, allowing you to prioritize regions with high cellularity for your analysis or model training.

Histolab Digital Pathology

Name: Histolab Digital Pathology
Author: pur3v4d3r

bypur3v4d3r

•

Ciencia de Datos y ML

Processes whole slide images (WSI) for digital pathology by automating tissue detection and tile extraction for machine learning pipelines.

Acerca de

Histolab is a specialized toolkit designed for digital pathology workflows, specifically handling gigapixel Whole Slide Images (WSI) in formats like SVS, TIFF, and NDPI. It enables researchers and developers to automatically detect tissue regions, apply sophisticated morphological filters, and extract informative tiles using random, grid, or score-based strategies. By simplifying the preprocessing of massive histological images, it provides a robust foundation for building deep learning models and computational pathology datasets.

Características Principales

Support for multi-format gigapixel Whole Slide Images (WSI) like SVS and TIFF
1 GitHub stars
Diverse tile extraction strategies including Random, Grid, and Score-based tiling
Integrated visualization tools for slide thumbnails, masks, and tile locations
Automated tissue detection and binary masking for background exclusion
Extensive library of morphological and image preprocessing filters

Casos de Uso

Preparing histological datasets for deep learning and AI model training
Segmenting tissue regions and excluding artifacts from large pathology slides
Extracting the most informative tiles from slides based on nuclei density or cellularity

Acerca de

Características Principales

Support for multi-format gigapixel Whole Slide Images (WSI) like SVS and TIFF
1 GitHub stars
Diverse tile extraction strategies including Random, Grid, and Score-based tiling
Integrated visualization tools for slide thumbnails, masks, and tile locations
Automated tissue detection and binary masking for background exclusion
Extensive library of morphological and image preprocessing filters

Casos de Uso

Preparing histological datasets for deep learning and AI model training
Segmenting tissue regions and excluding artifacts from large pathology slides
Extracting the most informative tiles from slides based on nuclei density or cellularity