cell-data-loader 0.0.3

Converts general images of cells into formats and labels for deep learning pipelines

Cell Data Loader

Cell Data Loader is a simple AI support tool in Python that can take in images of cells (or other image types) and output them with minimal effort to formats that can be read by Pytorch (Tensor) or Tensorflow (Numpy) format. With Cell Data Loader, users have the option to output their cell images as whole images, sliced images, or, with the support of CellPose, segment their images by cell and output those individually.

To install Cell Data Loader, simply type into a standard UNIX terminal

pip install cell-data-loader

The simplest way to use Cell Data Loader is to instantiate a dataloader as such:

from cell_data_loader import CellDataloader

imfolder = '/path/to/my/images'

dataloader = CellDataloader(imfolder)

for image in dataloader:
	...

And viola!

Lists of files are also supported:

imfiles = ['/path/to/image1.png','/path/to/image2.png','/path/to/image3.png']

dataloader = CellDataloader(imfiles)

for image in dataloader:
	...

Labels

Cell Data Loader has a few ways to support image labels. The simplest is whole images that are located in different folders, with each folder representing a label. This can be supported via the following:

imfolder1 = '/path/to/my/images'
imfolder2 = '/path/to/your/images'

dataloader = CellDataloader(imfolder1,imfolder2)

for label,image in dataloader:
	...

Alternatively, if you have one folder or file list with images that have different naming conventions, a regex match is supported:

imfiles = ['/path/to/CANCER_image1.png','/path/to/CANCER_image2.png','/path/to/CANCER_image3.png','/path/to/HEALTHY_image1.png','/path/to/HEALTHY_image2.png','/path/to/HEALTHY_image3.png']

dataloader = CellDataloader(imfiles,label_regex = ["CANCER","HEALTHY"])
for label,image in dataloader:
	...

Arguments

Additional arguments taken by Cell Data Loader include

imfolder = '/path/to/folder'

dataloader = CellDataloader(imfolder,
			dim = (64,64),
			batch_size = 32,
			dtype = "numpy", # Can also be "torch"
			label_regex = None,
			n_channels = 3, # This is detected in the first read image by default, if not provided; it re-samples all images to force this number of channels
			match_labels = False, # Outputs proportional amounts of each label in the dataset
			)

Dependencies

Strict dependencies:

numpy
torch
torchvision
opencv-python>=4.5.4
slideio==2.4.1
scipy
scikit-image
pillow

Soft dependencies:

CellPose # For cell segmentation support
Tensorflow

Note that some of the dependencies are not strict and vary depending on usage. Numpy is a hard requirement, but Tensorflow is not if the user only uses the Torch capabilities. If the user attempts to load cell images in "cell" mode without a working Cellpose installation, CellDataLoader will throw an error. Cellpose needs be be installed separately to use "cell" mode:

pip install cellpose

And GPU integration is a separate matter.