histolab

You can extract tiles from whole slide images using random, grid, or score-based tiling strategies. This process automates the extraction of smaller regions from large WSI files, preparing data efficiently for deep learning analysis.

Automatically detect tissue regions in whole slide images by applying tissue segmentation masks. This identifies valid tissue areas and filters out background artifacts, ensuring only informative regions are processed for downstream analysis.

Yes, you need OpenSlide to process whole slide images for histopathology workflows. The processing requires OpenSlide, numpy, scikit-image, and Pillow to manipulate and analyze WSI data for tissue segmentation and tile extraction.

The best way to prepare whole slide image data for deep learning models is to apply tissue detection and extract informative tiles. Using score-based tiling ensures only high-quality regions are selected, optimizing the dataset for training.

Yes, you can visualize tissue masks and tile locations on whole slide images. The visualization capabilities allow you to inspect slides, generated masks, extraction quality, and tile placements to verify the preprocessing results.

You can apply various image processing filters to whole slide images for quality control and preprocessing. These filters help refine the extracted tiles and tissue masks before feeding the data into deep learning pipelines.