EmbedSeg 0.2.1

EmbedSeg provides automatic detection and segmentation of objects in microscopy images

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.

• One could install EmbedSeg with pip:

conda create -n EmbedSegEnv python==3.7
conda activate EmbedSegEnv
python3 -m pip install EmbedSeg

and then install pytorch:

conda install pytorch==1.1.0 torchvision==0.3.0 cudatoolkit=10.0 -c pytorch

• Alternately, one could use the environment.yml file (this would also install pytorch, torchvision and cudatoolkit). Create a new environment using :

conda env create -f path/to/environment.yml.

Getting Started

Look in the examples directory, and try out one of the provided notebooks. 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 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
            └───...