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DeepMuCS: A framework for co-culture microscopic image analysis: from generation to segmentation
German Research Center for Artificial Intelligence (DFKI) GmbH, Kaiserslautern, Germany.
German Research Center for Artificial Intelligence (DFKI) GmbH, Kaiserslautern, Germany.
German Research Center for Artificial Intelligence (DFKI) GmbH, Kaiserslautern, Germany.
German Research Center for Artificial Intelligence (DFKI) GmbH, Kaiserslautern, Germany.
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2022 (English)In: 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), IEEE, 2022, p. 1-4Conference paper, Published paper (Refereed)
Abstract [en]

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 in addition to cell segmentation in co-culture is essential for understanding the cellular mechanisms associated with disease states. In drug development, biologists are more interested in co-culture models because they replicate the tumor environment in vivo better than the monoculture models. Additionally, they have a measurable effect on cancer cell response to treatment. Co-culture models are critical for designing a drug with maximum efficacy on cancer while minimizing harm to the rest of the body. In the past, there existed 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 allowed us to perform experiments for cell-type-aware segmentation. However, it is composed of microscopic images in a monoculture environment. This paper presents a framework for co-culture microscopic image data generation, where each image can contain multiple cell cultures. The framework also presents a pipeline for culture-dependent cell segmentation in co-culture microscopic images. The extensive evaluation revealed that it is possible to achieve cell-type aware segmentation in co-culture microscopic images with good precision.

Place, publisher, year, edition, pages
IEEE, 2022. p. 1-4
Keywords [en]
biomedical, cell segmentation, co-culture, deep learning, healthcare
National Category
Medical Biotechnology (with a focus on Cell Biology (including Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy)
Identifiers
URN: urn:nbn:se:umu:diva-201641DOI: 10.1109/BHI56158.2022.9926936ISI: 000895865900089Scopus ID: 2-s2.0-85143072914ISBN: 9781665487917 (electronic)OAI: oai:DiVA.org:umu-201641DiVA, id: diva2:1718515
Conference
2022 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2022, September 27-30, 2022
Available from: 2022-12-13 Created: 2022-12-13 Last updated: 2023-09-05Bibliographically approved

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Trygg, JohanSjögren, Rickard

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