GlassCut¶

A lightweight, extensible toolkit for preprocessing and analyzing whole slide images (WSI) in digital pathology.

GlassCut combines efficient WSI reading, and grid tiling into a modular pipeline, with optional GPU acceleration via CuCim, following optimized preprocessing workflows [Horst2024], and [Marcolini2022]. It is designed to be both usable out-of-the-box and easily extensible for custom research workflows.

[1] Multi-backend WSI reading

OpenSlide (CPU) and CuCim (GPU) with automatic fallback

Working with Slides

[2] Grid tiling

Regular grid extraction with overlap, prescreening, and parallelism

Tiling Strategies

[3] Tissue detection

Otsu-based segmentation with a pluggable detector interface

Tissue Detection

[4] Stain normalization

Macenko and Reinhardt methods with custom normalizer support

Stain Normalization

[5] Dataset generation

Multi-slide extraction with checkpoint/resume and metadata

Dataset Generation

[6] Sample data

Built-in downloader for public WSI samples (TCGA [TCGA], GDC [GDC], Aperio [Aperio], IDR [IDR]); adapted from HistoLab [Marcolini2022]

Sample Data

Quick Start¶

from glasscut import Slide, GridTiler

slide = Slide("slide.svs")

tiler = GridTiler(
   tile_size=(512, 512),
   magnification=20
)

for tile in tiler.extract(slide):
   tile.save(f"tiles/{tile.coords}.png")

slide.close()

See the Quick Start guide for more details.

Indices and Tables¶

References¶

[TCGA]

The Cancer Genome Atlas Program (TCGA). National Cancer Institute, National Institutes of Health. https://www.cancer.gov/tcga

[IDR]

Williams, E., Moore, J., Li, S. W., et al. (2017). Image Data Resource: A bioimage data integration and publication platform. Nature Methods, 14, 775–776. https://doi.org/10.1038/nmeth.4326

[Aperio]

Leica Biosystems. Aperio AT2 — Digital Pathology Scanner. https://www.leicabiosystems.com/digital-pathology/scan/aperio-at2/

[GDC]

Genomic Data Commons (GDC). National Cancer Institute. https://portal.gdc.cancer.gov

[Marcolini2022] (1,2)

Marcolini, A., Bussola, N., Arbitrio, E., Amgad, M., Jurman, G., & Furlanello, C. (2022). histolab: A Python library for reproducible Digital Pathology preprocessing with automated testing. SoftwareX, 20(101237). https://doi.org/10.1016/j.softx.2022.101237

[Horst2024]

Hörst, F., Schaheer, S. H., Baldini, G., Bahnsen, F. H., Egger, J., & Kleesiek, J. (2024). Accelerating Artificial Intelligence-based Whole Slide Image Analysis with an Optimized Preprocessing Pipeline. Bildverarbeitung für die Medizin 2024, pp. 356–361. https://doi.org/10.1007/978-3-658-44037-4_91

[kaiko2024]

kaiko.ai et al. (2024). Towards Large-Scale Training of Pathology Foundation Models. arXiv preprint arXiv:2404.15217. https://arxiv.org/abs/2404.15217