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

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posted on 2022-02-22, 03:59 authored 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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Submitting Author's Institution

German Research Center for Artificial Intelligence (DFKI) GmbH

Submitting Author's Country

  • Germany