How to?

This section provides useful tips when using LazySlide.

Tissue segmentation

My tissues look very vague, how do I segment them?

You can either try the entropy-based segmentation:

zs.pp.find_tissues(wsi, method="entropy")

Or you can try the deep learning-based segmentation:

zs.seg.tissue(wsi)

Feature extraction

How do I extract dense/patch features?

You can simply set dense=True when running feature extraction, notice that this only works for ViT-based models:

zs.tl.feature_extraction(wsi, model="uni", dense=True)

How do I control the pooling behavior of the extracted features?

There are currently two options for pooling the extracted features: “cls” and “cls_patch_mean”. But this only works for ViT-based models.

• “cls”: This option uses the [CLS] token as the pooled representation of the tissue tile. Transformer-based models use the [CLS] token for classification tasks.

• “cls_patch_mean”: This option concatenates the [CLS] token and the mean of patch tokens. This combines both global context (the CLS token) and local details (the patch tokens).

zs.tl.feature_extraction(wsi, model="uni", pool_mode="cls_patch_mean")

How do I extract features for cells?

Simply point the tile_key to “cells” when running feature extraction.

zs.tl.feature_extraction(wsi, model="uni", tile_key="cells")

For ViT-based cell segmentation models, you can get both the cell segmentation and cell features at the same time.

zs.seg.cell_types(wsi, model="histoplus", extract_features=True)