This repository contains the code for ACCDor, an anterior chamber cell detector.
- Create a new conda environment with Python 3.9:
conda create -n accdor python=3.9
- Activate the conda environment:
conda activate accdor # sometimes could be `source activate accdor`
- Install the required dependencies:
pip install -r requirements.txt
ACCDor requires a pre-trained ViT model. Download the model from the ViT-H SAM model link. The link is provided in the SAM repository. Put the pre-trained model into the models folder
To process an image, segment the AC area, and detect cells, run the following command:
python -m apps.detect_cell
By default, this command will process the image located at data/example/example1.jpeg
. The output will be saved in the data/output/{image_name}
directory, where {image_name}
is the name of the example image (in this case, example1
).
After running apps.detect_cell
, intermediate stage images will also be generated. Below are examples of the generated images for the sample input image.
arXiv: https://arxiv.org/abs/2406.17577
To cite ACCDor in publications, please use:
@article{chen2024advancing,
title={Advancing Cell Detection in Anterior Segment Optical Coherence Tomography Images},
author={Boyu Chen and Ameenat L. Solebo and Paul Taylor},
year={2024},
journal={arXiv preprint arXiv:2406.17577}
}
Thanks to the support of AWS Doctoral Scholarship in Digital Innovation, awarded through the UCL Centre for Digital Innovation. We thank them for their generous support.