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

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posted on 22.02.2022, 03:59 by Nabeel KhalidNabeel Khalid, Mohammadmahdi Koochali, Vikas Rajashekhar, Mohsin Munir, Christoffer EdlundChristoffer Edlund, Timothy Jackson, Johan Trygg, Rickard SjögrenRickard Sjögren, Andreas Dengel, Sheraz Ahmed
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 extensive evaluation, it was revealed that it is possible to achieve good quality cell-type aware segmentation in mono- and co-culture microscopic images.

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Email Address of Submitting Author

nabeel.khalid@dfki.de

Submitting Author's Institution

German Research Center for Artificial Intelligence (DFKI) GmbH

Submitting Author's Country

Germany