Joined segmentation of nuclei and cells - Inria - Institut national de recherche en sciences et technologies du numérique
Poster Année : 2024

Joined segmentation of nuclei and cells

Résumé

Deep learning has revolutionized instance segmentation, i.e. the precise localization of individual objects. In microscopy, the two most popular approaches, Stardist and Cellpose, are now used in routine to segment nuclei or cells. However, some specific applications might benefit from the segmentation of both nuclei and cells. For example, multi/hyperplexing imaging show cells associated with nuclear, membranar and cytoplasmic markers. The identification of nuclear and cytoplasmic masks can improve cell phenotyping, consisting of matching cells with their associated markers. In this study, we propose a new approach to jointly segment nuclei and cells. We take advantage of TissueNet, a very large dataset with images showing nuclear and cytoplasmic channels for which we ensure that each nuclear mask is associated with a cell mask. We then train a deep learning network based on the Cellpose architecture to jointly segment nuclei and cells and evaluate its performance when compared to the separate segmentation of nuclei and cells applied to the same 2-channels images.
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Dates et versions

hal-04874487 , version 1 (08-01-2025)

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  • HAL Id : hal-04874487 , version 1

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Gabriel Ravelomanana, Charles Kervrann, Thierry Pécot. Joined segmentation of nuclei and cells. I2K 2024 - Conference From Images to Knowledge, Oct 2024, Milan, Italy. , pp.1-1, 2024. ⟨hal-04874487⟩
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