- Introduction
- Dependencies
- Getting Started
- Datasets
- Training & Inference on your data
- Issues
- Citation
- Acknowledgements
This repository hosts the version of the code used for the publication Embedding-based Instance Segmentation of Microscopy Images.
We refer to the techniques elaborated in the publication, here as EmbedSeg. EmbedSeg is a method to perform instance-segmentation of objects in microscopy images, based on the ideas by Neven et al, 2019.
With EmbedSeg, we obtain state-of-the-art results on multiple real-world microscopy datasets. EmbedSeg has a small enough memory footprint (between 0.7 to about 3 GB) to allow network training on virtually all CUDA enabled hardware, including laptops.
One could execute these lines of code to run this branch with GPU support:
mamba create -n EmbedSeg python
mamba activate EmbedSeg
mamba install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
git clone https://github.com/juglab/EmbedSeg.git
cd EmbedSeg
pip install -e .
For CPU support, one could execute the following lines of code:
mamba create -n EmbedSeg python
mamba activate EmbedSeg
pip install torch torchvision
git clone https://github.com/juglab/EmbedSeg.git
cd EmbedSeg
Look in the examples directory, and try out the DSB-2018 notebooks for 2D images or Mouse-Organoid-Cells-CBG notebooks for volumetric (3D) images. Please make sure to select Kernel > Change kernel to EmbedSegEnv.
3D datasets are available as release assets here.
*.tif-type images and the corresponding masks should be respectively present under images and masks, under directories train, val and test. (In order to prepare such instance masks, one could use the Fiji plugin Labkit as suggested here). The following would be a desired structure as to how data should be prepared.
$data_dir
└───$project-name
|───train
└───images
└───X0.tif
└───...
└───Xn.tif
└───masks
└───Y0.tif
└───...
└───Yn.tif
|───val
└───images
└───...
└───masks
└───...
|───test
└───images
└───...
└───masks
└───...
If you encounter any problems, please file an issue along with a detailed description.
If you find our work useful in your research, please consider citing:
@InProceedings{lalit2021embedseg,
title = {Embedding-based Instance Segmentation in Microscopy},
author = {Lalit, Manan and Tomancak, Pavel and Jug, Florian},
booktitle = {Proceedings of the Fourth Conference on Medical Imaging with Deep Learning},
pages = {399--415},
year = {2021},
editor = {Heinrich, Mattias and Dou, Qi and de Bruijne, Marleen and Lellmann, Jan and Schläfer, Alexander and Ernst, Floris},
volume = {143},
series = {Proceedings of Machine Learning Research},
month = {07--09 Jul},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v143/lalit21a/lalit21a.pdf},
url = {https://proceedings.mlr.press/v143/lalit21a.html},
}and
@article{lalit2022mia,
title = {EmbedSeg: Embedding-based Instance Segmentation for Biomedical Microscopy Data},
journal = {Medical Image Analysis},
volume = {81},
pages = {102523},
year = {2022},
issn = {1361-8415},
doi = {https://doi.org/10.1016/j.media.2022.102523},
url = {https://www.sciencedirect.com/science/article/pii/S1361841522001700},
author = {Manan Lalit and Pavel Tomancak and Florian Jug},
}The authors would like to thank the Scientific Computing Facility at MPI-CBG, thank Matthias Arzt, Joran Deschamps and Nuno Pimpao Martins for feedback and testing. Alf Honigmann and Anna Goncharova provided the Mouse-Organoid-Cells-CBG data and annotations. Jacqueline Tabler and Diana Afonso provided the Mouse-Skull-Nuclei-CBG dataset and annotations. This work was supported by the German Federal Ministry of Research and Education (BMBF) under the codes 031L0102 (de.NBI) and 01IS18026C (ScaDS2), and the German Research Foundation (DFG) under the code JU3110/1-1(FiSS) and TO563/8-1 (FiSS). P.T. was supported by the European Regional Development Fund in the IT4Innovations national supercomputing center, project number CZ.02.1.01/0.0/0.0/16013/0001791 within the Program Research, Development and Education.
The authors would like to thank the authors of StarDist repository for several useful, helper functions. The authors would also like to thank Sahar Kakavand and Marco Dalla Vecchia for feedback on notebooks.
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