This repository contains a Python package, biopsy, for preprocessing OpenSlide image files (.ndpi files) and their corresponding annotations (.ndpa files) for use with Machine Learning segmentation models. The package serves three main purposes:

1. Since the OpenSlide image files are often very large, it's not possible to build Machine Learning models that process the entire full resolution image at once. Instead, the original image can be divided into smaller tiles each of which can then be fed to the Machine Learning model separately.
2. When training a segmentation model, an image that defines the segments is required. This is called the mask and it would be the expected output that the model would learn to output given a tile. The .ndpa files are XML files which define the segments as points and lines between them. The biopsy package can, for every tile that it creates, create a corresponding mask image from the .ndpa file.
3. Finally, the package can handle very basic image augmentation by rotating the tiles and corresponding masks.

Below is an example showing a tile and its corresponding mask. Finally, just for illustration, the mask is shown pasted onto the original tile to show how they line up.

Tile	Mask	Mask on tile

Before you can use the biopsy package, you need to install OpensSide. Since OpenSlide is not a python package, it is not installed automatically when installing biopsy. Instructions for installing OpenSlide can be found here at https://openslide.org/download/. If you're on a Mac, you can install OpenSlide using Homebrew as follows:

brew install openslide

pip3 install -e .

After installing biopsy, you can run it from the the command-line as follows:

biopsy preprocess INPUT_FOLDER OUTPUT_FOLDER

The command will find all .ndpi files in the specified INPUT_FOLDER which have a corresponding .ndpa file and create tile images and mask images in the specified OUTPUT_FOLDER.

In addition to the two positional arguments mentioned above, you can also specify optional arguments:

• -h or --help: Show help message.
• -l LEVEL or --level LEVEL: Zoom level to use for extraction of images from slides. Defaults to 2.
• -s TILE_SIZE or --tile-size TILE_SIZE: Size of output tiles in pixels. Defaults to 512.
• -o OVERLAP or --overlap OVERLAP: Fraction of image to overlap tiles. Defaults to 0.0.
• --rotate: Create augmented images by rotating the original images.

Alternatively, you can run the Jupyter notebook named Preprocess located in the notebooks folder.

For this package to be useful, the following needs to be implemented:

1. Some kind of segmentation model, possibly a UNet, that can be trained on the generated images.
2. Some way to convert segmenations created by the segmentation model to .ndpa files to allow them to be loaded back into the slide viewer / editor software.

• For some hints on how to interpret the .ndpa files, see: https://openslide-users.andrew.cmu.narkive.com/bouE1LwV/nanozoomer-annotation-coordinate-system-to-openslide-pixels.