Speaker
Description
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.
| Authors | Gabriel Ravelomanana, Charles Kervrann and Thierry Pécot* |
|---|---|
| Keywords | instance segmentation, deep learning, nuclei and cell segmentation |
