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{ | ||
"name": "Cellpose", | ||
"short_name": "cellpose", | ||
"metadata": { | ||
"description": "Cellpose is a generalist model for cell and nucleus segmentation.", | ||
"url": "https://cellpose.readthedocs.io/en/latest/", | ||
"repo": "https://github.com/MouseLand/cellpose", | ||
"pubs": [ | ||
{ | ||
"info": "Cellpose v1", | ||
"url": "https://doi.org/10.1038/s41592-020-01018-x", | ||
"title": "Cellpose: a generalist algorithm for cellular segmentation", | ||
"doi": "10.1038/s41592-020-01018-x", | ||
"authors": [ | ||
{ | ||
"name": "Carsen Stringer", | ||
"affiliation": "HHMI Janelia Research Campus, Ashburn, VA, USA" | ||
}, | ||
{ | ||
"name": "Tim Wang", | ||
"affiliation": "HHMI Janelia Research Campus, Ashburn, VA, USA" | ||
}, | ||
{ | ||
"name": "Michalis Michaelos", | ||
"affiliation": "HHMI Janelia Research Campus, Ashburn, VA, USA" | ||
}, | ||
{ | ||
"name": "Marius Pachitariu", | ||
"affiliation": "HHMI Janelia Research Campus, Ashburn, VA, USA" | ||
} | ||
] | ||
}, | ||
{ | ||
"info": "Cellpose v2", | ||
"url": "https://doi.org/10.1038/s41592-022-01663-4", | ||
"title": "Cellpose 2.0: how to train your own model", | ||
"doi": "10.1038/s41592-022-01663-4", | ||
"authors": [ | ||
{ | ||
"name": "Marius Pachitariu", | ||
"affiliation": "HHMI Janelia Research Campus, Ashburn, VA, USA" | ||
}, | ||
{ | ||
"name": "Carsen Stringer", | ||
"affiliation": "HHMI Janelia Research Campus, Ashburn, VA, USA" | ||
} | ||
] | ||
}, | ||
{ | ||
"info": "Cellpose v3", | ||
"url": "https://www.biorxiv.org/content/10.1101/2024.02.10.579780v2", | ||
"title": "Cellpose3: one-click image restoration for improved cellular segmentation", | ||
"doi": "10.1101/2024.02.10.579780", | ||
"authors": [ | ||
{ | ||
"name": "Carsen Stringer", | ||
"affiliation": "HHMI Janelia Research Campus, Ashburn, VA, USA" | ||
}, | ||
{ | ||
"name": "Marius Pachitariu", | ||
"affiliation": "HHMI Janelia Research Campus, Ashburn, VA, USA" | ||
} | ||
] | ||
} | ||
] | ||
}, | ||
"versions": { | ||
"cyto3": { | ||
"tasks": { | ||
"cyto": { | ||
"location":"https://www.cellpose.org/models/cyto3" | ||
} | ||
} | ||
}, | ||
"nuclei": { | ||
"tasks": { | ||
"nuclei": { | ||
"location": "https://www.cellpose.org/models/nucleitorch_0" | ||
} | ||
} | ||
}, | ||
"cyto1": { | ||
"tasks": { | ||
"cyto": { | ||
"location": "https://www.cellpose.org/models/cytotorch_0" | ||
} | ||
} | ||
}, | ||
"cyto2": { | ||
"tasks": { | ||
"cyto": { | ||
"location":"https://www.cellpose.org/models/cyto2torch_0" | ||
} | ||
} | ||
} | ||
}, | ||
"params": [ | ||
{ | ||
"name": "Diameter", | ||
"arg_name": "diameter", | ||
"value": 0, | ||
"tooltip": "Diameter of the cells in pixels. If None or 0, Cellpose will try to estimate it. Setting the value may improve results. See https://cellpose.readthedocs.io/en/latest/settings.html#diameter for details." | ||
}, | ||
{ | ||
"name": "Segment Channel", | ||
"arg_name": "segment_channel", | ||
"value": 0, | ||
"tooltip": "What channel to segment on. 0=grayscale, 1=red, 2=green, 3=blue. See https://cellpose.readthedocs.io/en/latest/settings.html#channels for details." | ||
}, | ||
{ | ||
"name": "Nucleus Channel", | ||
"arg_name": "nucleus_channel", | ||
"value": 0, | ||
"tooltip": "What channel contains nucleus. 0=grayscale, 1=red, 2=green, 3=blue. Unused for 'nuclei' model, where nuclei are in the segment channel. See https://cellpose.readthedocs.io/en/latest/settings.html#channels for details." | ||
}, | ||
{ | ||
"name": "3D Segmentation", | ||
"arg_name": "do_3D", | ||
"value": false, | ||
"tooltip": "Whether to do 3D segmentation. If True, will try to segment in 3D (XY, XZ, YZ). If False, will segment in 2D and combine with stitch_threshold." | ||
}, | ||
{ | ||
"name": "Stitch Threshold", | ||
"arg_name": "stitch_threshold", | ||
"value": 0.0, | ||
"tooltip": "Threshold for stitching 2D segmentations together. Value is the IoU that constitutes an overlap and thus merge. Only used if do_3D is False and >0." | ||
}, | ||
{ | ||
"name": "Cellprob Threshold", | ||
"arg_name": "cellprob_threshold", | ||
"value": 0.0, | ||
"tooltip": "Threshold for flows to determine ROIs. Decrease to find more & larger ROIs, increase if too many ROIs (esp. in dim regions). See https://cellpose.readthedocs.io/en/latest/settings.html#cellprob-threshold for details." | ||
}, | ||
{ | ||
"name": "Flow Threshold", | ||
"arg_name": "flow_threshold", | ||
"value": 0.4, | ||
"tooltip": "Threshold for flows to determine ROIs. Not used for 3D. See https://cellpose.readthedocs.io/en/latest/settings.html#cellprob-threshold for details." | ||
}, | ||
{ | ||
"name": "Num Iterations", | ||
"arg_name": "niter", | ||
"value": 0, | ||
"tooltip": "Number of iterations to simulate dynamics for. By default (0/None), proportional to ROI diameter. See https://cellpose.readthedocs.io/en/latest/settings.html#number-of-iterations-niter for details." | ||
}, | ||
{ | ||
"name": "Anisotropy", | ||
"arg_name": "anisotropy", | ||
"value": null, | ||
"dtype": "float", | ||
"tooltip": "Optional rescaling factor for Z (e.g. set to 2.0 if Z is sampled half as dense as X or Y)." | ||
}, | ||
{ | ||
"name": "Channel axis", | ||
"arg_name": "channel_axis", | ||
"value": null, | ||
"dtype": "int", | ||
"tooltip": "Set the axis where channels are stored. 'None' means it tries to figure it out - useful if no/poor metadata and not loading properly." | ||
}, | ||
{ | ||
"name": "Z axis", | ||
"arg_name": "z_axis", | ||
"value": null, | ||
"dtype": "int", | ||
"tooltip": "Set the axis where Z slices are stored. 'None' means it tries to figure it out - useful if no/poor metadata and not loading properly." | ||
}, | ||
{ | ||
"name": "Batch size", | ||
"arg_name": "batch_size", | ||
"value": 64, | ||
"tooltip": "Number of 224x224 patches to run simultaneously on the GPU. Can make smaller or bigger depending on GPU memory usage." | ||
}, | ||
{ | ||
"name": "Minimum size", | ||
"arg_name": "min_size", | ||
"value": 15, | ||
"tooltip": "Size (in pixels) of ROIs/to remove if lower. 0 will not remove any masks." | ||
} | ||
] | ||
} |