
View on bioimage.io # vit_l_lm Segment Anything for Light Microscopy This is a Segment Anything model that was specialized for light microscopy with micro_sam. This model uses a vit_l vision transformer as image encoder. Segment Anything is a model for interactive and automatic instance segmentation. We improve it for light microscopy by finetuning on a large and diverse microscopy dataset. It should perform well for cell and nucleus segmentation in fluorescent, label-free and other light microscopy datasets. See the dataset overview for further informations on the training data and the micro_sam documentation for details on how to use the model for interactive and automatic segmentation. Validation The easiest way to validate the model is to visually check the segmentation quality for your data. If you have annotations you can use for validation you can also quantitative validation, see here for details. Please note that the required quality for segmentation always depends on the analysis task you want to solve.
Finetuned Segment Anything Model for Microscopy (Uploaded via https://bioimage.io)
bioimage.io:model, instance-segmentation, bioimage.io, backup.bioimage.io, segment-anything
bioimage.io:model, instance-segmentation, bioimage.io, backup.bioimage.io, segment-anything
| 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 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| 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 |
