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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: segmentation software, data processing software, software application, image analysis software, software resource, image processing software
Keywords: segmentation, identifying cells, segmenting tissue, universal models, tissue image, cell segmentation
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