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| Hauptseite > Publikationsdatenbank > The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions |
| Hauptseite > Publikationsdatenbank > The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions |
| Preprint | FZJ-2023-03643 |
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2023
arXiv
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Please use a persistent id in citations: doi:10.48550/ARXIV.2308.05864 doi:10.48550/arXiv.2308.05864 doi:10.34734/FZJ-2023-03643
Abstract: Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyperparameters in different experimental settings. Here, we present a multi-modality cell segmentation benchmark, comprising over 1500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods, but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging.
Keyword(s): Image and Video Processing (eess.IV) ; Computer Vision and Pattern Recognition (cs.CV) ; Machine Learning (cs.LG) ; Quantitative Methods (q-bio.QM) ; FOS: Electrical engineering, electronic engineering, information engineering ; FOS: Computer and information sciences ; FOS: Biological sciences
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