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DeepMuCS: A Framework for Mono- & Co-culture Microscopic Image Analysis: From Generation to Segmentation
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  • Nabeel Khalid ,
  • Mohammadmahdi Koochali ,
  • Vikas Rajashekhar ,
  • Mohsin Munir ,
  • Christoffer Edlund ,
  • Timothy Jackson ,
  • Johan Trygg ,
  • Rickard Sjögren ,
  • Andreas Dengel ,
  • Sheraz Ahmed
Nabeel Khalid
German Research Center for Artificial Intelligence (DFKI) GmbH

Corresponding Author:[email protected]

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Mohammadmahdi Koochali
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Vikas Rajashekhar
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Mohsin Munir
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Christoffer Edlund
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Timothy Jackson
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Johan Trygg
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Rickard Sjögren
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Andreas Dengel
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Sheraz Ahmed
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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.