Can I filter out background and artifacts from the slides?

Yes, Histolab includes specialized masking tools like TissueMask and BiggestTissueBoxMask to automatically isolate tissue regions and exclude background or pen marks.

Is this skill suitable for preparing deep learning datasets?

Absolutely. Histolab is specifically designed to automate the tile extraction and preprocessing steps required to feed gigapixel images into deep learning frameworks.

How does ScoreTiler differ from GridTiler?

GridTiler extracts tiles systematically across the entire tissue area in a grid, while ScoreTiler ranks and selects only the most informative tiles based on metrics like nuclei density.

What slide formats does Histolab support?

Histolab supports major gigapixel formats commonly used in pathology, including SVS, TIFF, and NDPI, facilitating easy integration with clinical data.

Histolab Digital Pathology

Name: Histolab Digital Pathology
Author: Activer007

byActiver007

데이터 과학 및 ML

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

소개

Histolab is a specialized toolkit designed for the complex requirements of digital pathology, enabling efficient processing of gigapixel whole slide images (WSI). It streamlines the workflow of identifying tissue regions, filtering artifacts, and extracting representative tiles using various strategies like random sampling, systematic grids, or nuclei-density scoring. This skill is essential for researchers and developers building computational pathology pipelines, as it handles diverse formats such as SVS, TIFF, and NDPI, and provides robust tools for creating high-quality datasets for medical AI models.

주요 기능

Automated tissue detection and segmentation using advanced morphological filters
0 GitHub stars
Multi-strategy tile extraction including Random, Grid, and Score-based methods
Built-in visualization for slide thumbnails, tissue masks, and tile locations
Informative tile ranking using specialized Nuclei and Cellularity scorers
Broad support for gigapixel formats including SVS, TIFF, and NDPI

사용 사례

Preparing large-scale datasets for training deep learning models in pathology
Automating region-of-interest (ROI) identification in whole slide images
Preprocessing histological slides to remove artifacts, background, and pen marks

소개

주요 기능

Automated tissue detection and segmentation using advanced morphological filters
0 GitHub stars
Multi-strategy tile extraction including Random, Grid, and Score-based methods
Built-in visualization for slide thumbnails, tissue masks, and tile locations
Informative tile ranking using specialized Nuclei and Cellularity scorers
Broad support for gigapixel formats including SVS, TIFF, and NDPI

사용 사례

Preparing large-scale datasets for training deep learning models in pathology
Automating region-of-interest (ROI) identification in whole slide images
Preprocessing histological slides to remove artifacts, background, and pen marks