cmbjhmnl/worm-swin

Repository for WormSwin: Instance segmentation of C. elegans using vision transformer as appeared in Nature Scientific Reports 2023.

WormSwin

Repository for WormSwin: Instance segmentation of C. elegans using vision transformer as appeared in Nature Scientific Reports 2023.

Example images taken from the paper.

We used MMDetection as toolbox for our network. Please follow their installation instructions. To manage our Python packages we used (mini)conda. Our environment YAML file to restore the package versions can be found in this repo. Python version 3.8, PyTorch 1.11 for CUDA 11.3 and mmcv-full 1.5.3 was used for our setup.

We trained on 4 Nvidia Tesla V100-SMX2 32 GB GPUs, 6 cores of an Intel Xeon Gold 6248 CPU @ 2.50 GHz and 100 GB of RAM. With a batch size of four (one image per GPU) and two workers per GPU, training for 36 epochs took ∼ 19 h.

Datasets

• WormSwin Datasets
• BBBC010

Citation

@article{deserno_wormswin_2023,
	title = {{WormSwin}: {Instance} segmentation of {C}. elegans using vision transformer},
	volume = {13},
	copyright = {2023 The Author(s)},
	issn = {2045-2322},
	shorttitle = {{WormSwin}},
	url = {https://www.nature.com/articles/s41598-023-38213-7},
	doi = {10.1038/s41598-023-38213-7},
	language = {en},
	number = {1},
	urldate = {2023-07-10},
	journal = {Scientific Reports},
	author = {Deserno, Maurice and Bozek, Katarzyna},
	month = jul,
	year = {2023},
	note = {Number: 1
Publisher: Nature Publishing Group},
	keywords = {Behavioural methods, Machine learning, Software},
	pages = {11021},
}