Automatic cell segmentation in microscopy images works well with the support of deep neural networks trained with full supervision. Collecting and annotating images, though, is not a sustainable solution for every new microscopy database and cell type. Instead, we assume that we can access a plethora of annotated image data sets from different domains (sources) and a limited number of annotated image data sets from the domain of interest (target), where each domain denotes not only different image appearance but also a different type of cell segmentation problem. We pose this problem as meta-learning where the goal is to learn a generic and adaptable few-shot learning model from the available source domain data sets and cell segmentation tasks. The model can be afterwards fine-tuned on the few annotated images of the target domain that contains different image appearance and different cell type. In our meta-learning training, we propose the combination of three objective functions to segment the cells, move the segmentation results away from the classification boundary using cross-domain tasks, and learn an invariant representation between tasks of the source domains. Our experiments on five public databases show promising results from 1- to 10-shot meta-learning using standard segmentation neural network architectures.

Link to full paper https://arxiv.org/abs/2007.01671

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1- Install necessary python modules in requirements.txt

2- Run run_preprocessing.py i.e. python run_preprocessing.py to download the datasets and preprocess them, in addition to extracting and preprocessing my 10 random selections.

3- Instructions to run training and evaluation are available with examples in Learning_main.py and Evaluation_main.py

The Pre-trained models can be downloaded from this link https://cloudstore.uni-ulm.de/s/YqD6or4DLyjF7ry

This project is licensed under the MIT license - see the License.md file for details

To cite this repository, please use the following citation:

@inproceedings{DBLP:conf/pkdd/DawoudHCB20,
 author = {Youssef Dawoud and
 Julia Hornauer and
 Gustavo Carneiro and
 Vasileios Belagiannis},
 title = {Few-Shot Microscopy Image Cell Segmentation},
 booktitle = {{ECML/PKDD} {(5)}},
 series = {Lecture Notes in Computer Science},
 volume = {12461},
 pages = {139--154},
 publisher = {Springer},
 year = {2020}
}