EmbedSeg
Introduction
This repository hosts the version of the code used for the preprint Embedding-based Instance Segmentation of Microscopy Images. For a short summary of the main attributes of the publication, please check out the project webpage.
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.
Citation
If you find our work useful in your research, please consider citing:
@misc{lalit2021embeddingbased,
title={Embedding-based Instance Segmentation of Microscopy Images},
author={Manan Lalit and Pavel Tomancak and Florian Jug},
year={2021},
eprint={2101.10033},
archivePrefix={arXiv},
primaryClass={eess.IV}
}
Dependencies
We have tested this implementation using pytorch version 1.1.0 and cudatoolkit version 10.0 on a linux OS machine.
In order to replicate results mentioned in the publication, one could use the same virtual environment (EmbedSeg_environment.yml) as used by us. Create a new environment, for example, by entering the python command in the terminal conda env create -f path/to/EmbedSeg_environment.yml.
Getting Started
Please open a new terminal window and run the following commands one after the other.
git clone https://github.com/juglab/EmbedSeg.git
cd EmbedSeg
conda env create -f EmbedSeg_environment.yml
conda activate EmbedSegEnv
python3 -m pip install -e .
python3 -m ipykernel install --user --name EmbedSegEnv --display-name "EmbedSegEnv"
cd examples
jupyter notebook
(In case conda activate EmbedSegEnv generates an error, please try source activate EmbedSegEnv instead). Next, look in the examples directory, and try out the dsb-2018 example set of notebooks (to begin with). Please make sure to select Kernel > Change kernel to EmbedSegEnv.
Training & Inference on your data
*.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 detailed here). These are cropped in smaller patches in the notebook 01-data.ipynb. 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
└───...
![[BUG] `workers` Parameter not Respected by DataLoaders](https://avatars.githubusercontent.com/u/1803297?v=4)
![TypeError: forward() missing 4 required positional arguments: 'prediction', 'instances', 'labels', and 'center_images'[BUG]](https://avatars.githubusercontent.com/u/62505127?v=4)
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