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URL: https://github.com/HMS-IDAC/UnMicst
Proper Citation: UnMICST (RRID:SCR_021050)
Description: Software tool as set of deep learning nuclei instance and semantic segmentation models that have been trained on 6 human tissue types: tonsil, small intestine, ovary, and cancers of brain, colon, prostate, and lung stained with Hoechst 33342 and combination of lamin B2 and nucleoporin98. Includes manually curated annotations as well as novel concept of introducing intentionally defocused and saturated images for robustness.
Synonyms: , Universal Models for Identifying Cells and Segmenting Tissue, UNet Model for Identifying Cells and Segmenting Tissue, UnMicst
Resource Type: data processing software, image analysis software, image processing software, segmentation software, software application, software resource
Keywords: segmentation, identifying cells, segmenting tissue, universal models, tissue image, cell segmentation
Availability: Free, Available for download, Freely available
Resource Name: UnMICST
Resource ID: SCR_021050
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Source: SciCrunch Registry (RRID:SCR_005400)
Description: Interactive portal for finding and submitting biomedical resources. Resources within SciCrunch have assigned RRIDs which are used to cite resources in scientific manuscripts. SciCrunch Registry, formerly NIF Registry, provides resources catalog. Allows to add new resources. Allows edit existing resources after registration. Curators are tasked with identifying and registering resources, examining data, writing configuration files to index and display data and keeping contents current.
URL: https://rrid.site/rin/sources/SCR_005400
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