DeepMuCS: A Framework for Mono- & Co-culture Microscopic Image Analysis: From Generation to Segmentation

Nabeel Khalid, Mohammadmahdi Koochali, Vikas Rajashekar, Mohsin Munir, Christoffer Edlund, Timothy R Jackson, Johan Tryggy, Rickard Sjögren, Andreas Dengel, Sheraz Ahmed

In: 2022 International Joint Conference on Neural Networks (IJCNN). International Joint Conference on Neural Networks (IJCNN-2022) July 18-23 Padova Italy IJCNN 2022.


Discrimination between cell types in the co-culture environment with multiple cell lines can assist in examining the interaction between different cell populations. Identifying different cell cultures along with segmentation in co-culture is essential for understanding the cellular mechanisms associated with disease states. Extracting the information from the co-culture models can help in quantifying the sub-population response to treatment conditions. In the past, there exists minimal progress related to cell-type aware segmentation in the monoculture and no development whatsoever for the co-culture. The introduction of the LIVECell dataset has provided us with the opportunity to perform experiments for cell-type aware segmentation. However, it is composed of microscopic images in a monoculture environment. In this paper, we have proposed a pipeline for coculture microscopic images data generation, where each image can contain multiple cell cultures. In addition, we have proposed a pipeline for culture-dependent cell segmentation in monoculture and co-culture microscopic images. Based on the extensive evaluation, it was revealed that it is possible to achieve good quality cell-type aware segmentation in mono- and co-culture microscopic images.


DeepMuCS_(19)_techRxiv.pdf (pdf, 3 MB )

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz