AviBloodCount

This software enables fully automated counting of blood cells, including erythrocytes as well as leukocytes, in avian blood samples. AviBloodCount takes whole slide images as input and outputs the overall cell counts of all areas determined as countable.

We provide our models and annotated data at data.uni-marburg.de.

Installation

We provide a docker file to create an environment containing all dependencies necessary to run our model on whole slide images stored in the SVS file format.

Install Docker

To install Docker, follow the instructions at docker.com

Build Docker image

To build the docker image go to the docker directory and run the following command:

docker build --build-arg="USER_ID=$(id -u)" -t umr-ds/avibloodcount .

Usage

Preparation

1. Download our trained models, i.e., efficientNet_B0.onnx and condInst_R101.pth from here to ./models.
2. Place the SVS files to be processed in ./input. We provide several example SVS files.

Run inference

Running the Docker image as follows automatically starts inference with default parameters on all SVS files located at ./data:

docker run --rm -it -v ../code:/code -v /path/to/data:/data --shm-size 8gb --name avibloodcount umr-ds/avibloodcount

To run with GPU support, do the following:

docker run --rm -it -v ../code:/code -v /path/to/data:/data --shm-size 8gb --name avibloodcount umr-ds/avibloodcount --gpu 0

In this example, the code will run on GPU with index 0.

You can modify several parameters by passing them trailing the docker run command. For example, you can set the thresholds used for the countability classification and the instance segmentation models via --cls_thresh and --det_thresh, respectively. All available parameters can be listed by issuing

docker run --rm -it -v ../code:/code -v /path/to/data:/data --shm-size 8gb --name avibloodcount umr-ds/avibloodcount --help

Scientific Usage & Citation

If you are using this software in academia, we'd appreciate if you cited our scientific research paper as follows:

Vogelbacher, M.; Strehmann, F.; Bellafkir, H.; Mühling, M.; Korfhage, N.; Schneider, D.; Rösner, S.; Schabo, D.G.; Farwig, N.; Freisleben, B. Identifying and Counting Avian Blood Cells in Whole Slide Images via Deep Learning. Birds 2024, 5, 48-66. https://doi.org/10.3390/birds5010004

@article{vogelbacher2024identifying,
  title = {{Identifying and Counting Avian Blood Cells in Whole Slide Images}},
  author = {Vogelbacher, Markus and Strehmann, Finja and Bellafkir, Hicham and M{\"u}hling, Markus and Korfhage, Nikolaus and Schneider, Daniel and R{\"o}sner, Sascha and Schabo, Dana G. and Farwig, Nina and Freisleben, Bernd},
  journal = {Birds},
  volume = {5},
  year = {2024},
  number = {1},
  pages = {48--66},
  url = {https://www.mdpi.com/2673-6004/5/1/4},
  issn = {2673-6004},
  keywords = {cell segmentation, bird blood analysis, microscopy images, blood smear images, object detection, ornithology},
  doi = {10.3390/birds5010004},
}