Bayesian Particle Instance Segmentation for Electron Microscopy Image Quantification
This repository contains the official implementation of bpartis — a Bayesian deep neural network for nanoparticle instance segmentation.
Try an in-browser demo on your electron microscopy images here.
If you would like to use a pretrained bpartis model, we strongly recommend using imagedataextractor to do so.
import cv2
from imagedataextractor.segment import ParticleSegmenter
image = cv2.imread('<path/to/image>') # PIL can also be used
segmenter = ParticleSegmenter()
segmentation, uncertainty, _ = segmenter.segment(image)More detailed information can be found in the imagedataextractor segmentation documentation.
If you are interested in training bpartis to reproduce our results, follow these steps:
- Clone the repository
git clone https://github.com/by256/bpartis.git- Install requirements
python3 -m pip install -r requirements.txt-
Download the EMPS dataset from here.
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Train the BPartIS model on the EMPS dataset.
python bpartis/train.py --data-dir=<path/to/emps/dir/> --device=cuda --epochs=300 --save-dir=bpartis/saved_models/If you use bpartis in your work, please cite the following work:
B. Yildirim, J. M. Cole, "Bayesian Particle Instance Segmentation for Electron Microscopy Image Quantification", J. Chem. Inf. Model. (2021) https://doi.org/10.1021/acs.jcim.0c01455
@article{doi:10.1021/acs.jcim.0c01455,
author = {Yildirim, Batuhan and Cole, Jacqueline M.},
title = {Bayesian Particle Instance Segmentation for Electron Microscopy Image Quantification},
journal = {Journal of Chemical Information and Modeling},
volume = {61},
number = {3},
pages = {1136-1149},
year = {2021},
doi = {10.1021/acs.jcim.0c01455},
note ={PMID: 33682402},
URL = {https://doi.org/10.1021/acs.jcim.0c01455}
}
This project was financially supported by the Science and Technology Facilities Council (STFC) and the Royal Academy of Engineering (RCSRF1819\7\10).