Pathology on the platform

Analyze histology and whole slide images computationally — open large WSI formats, cut them into quality-filtered tiles, detect and measure tissue regions, and extract deep-learning features for downstream analysis. Reach for these tools when your data is digital pathology imagery and you want tile-level and tissue-level readouts at the slide scale. They sit in the tissue-scale, translational analysis arc rather than the small-molecule chemistry pipeline. See the Cell & Tissue overview for where this fits.

What pathology analysis does

You give the tool a whole slide image stored in the platform's workspace and pick a workflow. The processor opens the slide (via histolab/OpenSlide, falling back to PIL for standard image formats) and runs the workflow you chose over the pixels, returning structured results plus downloadable artifacts.

The three operational workflows are:

• Tile extraction — cut the slide into a grid of tiles, filter out background and blurry tiles, and package the survivors as a downloadable archive. This is the default and the foundation for most tile-based pipelines.
• Tissue detection — segment tissue from background across the slide, compute the tissue-covered percentage, and return bounding-box regions plus a mask image.
• Feature extraction — run each retained tile through a pretrained ResNet-50 and emit a per-tile deep-learning feature matrix for clustering, classification, or similarity work.

The schema also lists classification and cell segmentation, but the backend does not implement them: a job that requests either fails with an "Unsupported workflow" error. For tissue typing or cell counts, export the tiles or feature matrix and run your own classifier.

Citations

Goode A, Gilbert B, Harkes J, Jukic D, Satyanarayanan M. OpenSlide: A vendor-neutral software foundation for digital pathology. Journal of Pathology Informatics. 2013;4:27. doi: 10.4103/2153-3539.119005

histolab (open-source software, no reference publication; the platform installs histolab>=0.6.0). https://github.com/histolab/histolab

He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016:770-778. doi: 10.1109/CVPR.2016.90

Reinhard E, Adhikhmin M, Gooch B, Shirley P. Color transfer between images. IEEE Computer Graphics and Applications. 2001;21(4):34-41. doi: 10.1109/38.946629

Macenko M, Niethammer M, Marron JS, Borland D, Woosley JT, Guan X, Schmitt C, Thomas NE. A method for normalizing histology slides for quantitative analysis. 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. 2009:1107-1110. doi: 10.1109/ISBI.2009.5193250

Vahadane A, Peng T, Sethi A, Albarqouni S, Wang L, Baust M, et al. Structure-Preserving Color Normalization and Sparse Stain Separation for Histological Images. IEEE Transactions on Medical Imaging. 2016;35(8):1962-1971. doi: 10.1109/TMI.2016.2529665

When to reach for it

• You explicitly have whole slide images (SVS, NDPI, TIFF) and want tiling or tissue detection.
• You mention histopathology or digital pathology image analysis and need cell- or tissue-level readouts.

Reach for a different tool when you want molecular analysis (use the omics tools) or a general drug-discovery / target-identification workflow — pathology analysis is specifically for slide imagery.

Key capabilities

• WSI processing: SVS, TIFF, NDPI, and standard image formats (PNG/JPG)
• Tile extraction: grid tiling with tissue-percentage and blur quality filters
• Tissue detection: Otsu segmentation, tissue-coverage percentage, and region bounding boxes
• Feature extraction: pretrained ResNet-50 features per tile (2048-dim)
• Stain normalization: reinhard, macenko, or vahadane

How it fits the platform

Pathology analysis runs on GPU (A10G), asynchronously, and can take 10–60 minutes depending on slide size and workflow. It reads the slide from workspace storage and writes its artifacts — tile archives, masks, or feature files — back for you to download or feed into downstream analysis. Results render on the canvas as an intelligence card.

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