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Trained models that can be used for the software microbeSEG. The models were trained on a dataset consisting of 826 320x320 px crops extracted from the Omnipose dataset (bact_fluor train, bact_phase train), the microbeSEG dataset, and the Cell Tracking Challenge datasets BF-C2DL-HSC, BF-C2DL-MuSC, Fluo-N2DL-HeLa, and PhC-C2DL-PSC. The training dataset is also provided. Mean AJI+ scores on a test dataset with 32 320x320 px crops: - boundary_model_adam: 0.769 - boundary_model_ranger: 0.783 - distance_model_adam: 0.802 - distance_model_ranger: 0.835 When using these models, please cite (and check if pre-prints have meanwhile been published in a journal/conference): - microbeSEG, - our method, - Omnipose, - the latest Cell Tracking Challenge publication: current paper. Omnipose data license: www.cellpose.org/dataset_omnipose Usage Just download all files into the directory published in the microbeSEG repository. The models can then be selected in the graphical user interface of microbeSEG.
nucleus segmentation, omero, deep learning, microbeSEG, cell segmentation
nucleus segmentation, omero, deep learning, microbeSEG, cell segmentation
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
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| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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