bioimageloader

Load bioimages for machine leaning applications

bioimageloader is a python library to make it easy to load bioimage datasets for machine learning and deep learning. Bioimages come in numerous and inhomogeneous forms. bioimageloader attempts to wrap them in unified interfaces, so that you can easily concatenate, perform image augmentation, and batch-load them.

bioimageloader provides

1. collections of interfaces for popular and public bioimage datasets
2. albumentations, which is the most popular and powerful image augmentation library, as a image augmentation library
3. compatibility with scikit-learn, tensorflow, and pytorch

Table of Contents

• Quick overview
  • Load a single dataset
  • Load multiple datasets
  • Batch-load datasets
• bioimageloader is not/does not
• Why bioimageloader
• Installation
• Documentation
• Available collections
• QnA
• Contributing
• Contact

Quick overview

1. Load a single dataset

   Load and iterate 2018 Data Science Bowl

   from bioimageloader.collections import DSB2018
   import albumentations as A
   
   transforms = A.Compose([
       A.RandomCrop(width=256, height=256),
       A.HorizontalFlip(p=0.5),
       A.RandomBrightnessContrast(p=0.2),
   ])
   dsb2018 = DSB2018('path/to/root_dir', transforms=transforms)
   for data in dsb2018:
       image = data['image']
       mask = data['mask']
   

2. Load multiple datasets

   Load DSB2018 and Triple Negative Breast Cancer (TNBC)

   from bioimageloader import datasets_from_config, ConcatDataset
   from bioimageloader.collections import DSB2018, TNBC
   import albumentations as A
   
   transforms = A.Compose([
       A.RandomCrop(width=256, height=256),
       A.HorizontalFlip(p=0.5),
       A.RandomBrightnessContrast(p=0.2),
   ])
   cfg = {
       'DSB2018': { 'root_dir': 'path/to/root_dir' },
       'TNBC'   : { 'root_dir': 'path/to/root_dir' },
   }
   datasets = datasets_from_config(cfg, transforms=transforms)
   cat = ConcatDataset(datasets)
   for meow in cat:
       image = meow['image']
       mask = meow['mask']
   

3. Batch-load dataset

   from bioimageloader import BatchDataloader
   
   call_cat = BatchDataloader(cat,
                              batch_size=16,
                              drop_last=True,
                              num_workers=8)
   for meow in call_cat:
       batch_image = meow['image']
       batch_mask = meow['mask']
   

bioimageloader is not/does not

• a full pipeline for ML/DL
• a hub to bioimage datasets (if it ever becomes one, it would be awesome though)
• transform or modify the original source
• does not provide direct links for downloading data

Why bioimageloader

bioimagesloader is a by-product of my thesis. This library collected bioimage datasets for machine learning and deep learning. I needed a lot of diverse bioimaes for self-supervised neural networks for my thesis. While I managed to find many great datasets, they all came with different folder structures and formats. In addition, I encountered many issues to load and process them, which were sometimes technical or just rooted from the nature of bioimages. For instances of technical issues, some datasets were missing one or two pairs of image and annotation, had broken files, had very specific file formats that cannot be easily read in python, or provided mask annotation not in image format but in .xml format. It was rather pain to deal with all these edge cases one by one. But anyway I did it and I thought it would be valuable to package and share it with community so that others do not have to suffer.

Installation

bioimageloader requires Python 3.8 or higher. Find more options at bioimageloader-docs:Installation

git clone https://github.com/LaboratoryOpticsBiosciences/bioimageloader.git
cd bioimageloader && pip install -e .

Documentation

Full documentation is available at bioimageloader-docs

Available collections

Go to bioimageloader-docs:Catalogue

QnA

Why no direct download link to each dataset?

bioimageloader provides only codes (interfaces) to load data but not data itself. It comes down to the license issue, since some bioimages may have a complicated procedure to get, for example reading and agreeing with terms. You still can find links to their project pages or papers, and you need to find a way to get the data following their instruction. Once you downloaded a dataset and unzipped it, (if it is supported by bioimageloader) you simply pass its root directory as the first argument to corresponding class from collections bioimageloader.collections.

Dataset that I want is not in the supported list

First of all, I named each dataset class rather arbitrary. Try to find the dataset you want with authors' names or with other keywords (if it has any), and you may find it having an unexpected name. If it is the case, I apologize for bad names.

If you still cannot find it. Then you have two options: either you do it yourself following the guideline (we have examples in documentation, please check them out and please consider contributing!) or you can file an issue so that the community can help.

Don't know how to write my own dataloader.

Writing a dataloader requires a bit of Python skills. No easy way. Please read templates carefully and see how others are implemented. File an issue, and I am willing to help.

How to run a ML/DL model?

bioimageloader only helps loading images/annotations, not running ML/DL models. Check out ZeroCostDL4Mic.

I want more granular control over datasets individually

Each bioimage dataset is very unique and it is natural that users want more controls and it was true for my work as well. Good news is that bioimageloader suggests a template that you can extend from and make a subclass in your liking. Bad news is that you need to know how to make a subclass in Python (not so bad I hope. I suppose that you may have knowledge of Python, if you want to develop ML/DL in Python anyway). I included some examples of subclassing for my use case. I hope that they are useful with the template.

Contributing

Find guide at bioimageloader-docs:Contributing

Contact

I am open to any suggestions and discussions. Contact me through github or email.

Seongbin Lim

• Homepage: https://sbinnee.github.io/
• Email: seongbin.lim at polytechnique.edu, sungbin246 at gmail.com