cellpose

cellpose is an anatomical segmentation algorithm written in Python 3. For support, please open an issue. We make pip installable releases of cellpose available on pypi. You can install it as pip install cellpose[gui].

Cellpose-SAM: superhuman generalization for cellular segmentation now available!

• run Cellpose-SAM in the cloud (no install) at Hugging Face.

• paper on biorxiv

• talk

See details on migrating from Cellpose 3 to 4 in Settings

Old releases:

Cellpose3: one-click image restoration for improved cellular segmentation

• paper on biorxiv

• thread

Cellpose 2.0: how to train your own model

• paper on biorxiv

• talk

• twitter thread

• human-in-the-loop training protocol video

Cellpose: a generalist algorithm for cellular segmentation

• paper on biorxiv (see figure 1 below) and in nature methods

• twitter thread

• Marius’s talk

Basics:

• Installation
  • Built-in model directory
  • M1-M3 Mac installation
  • AMD GPU ROCm installation
  • Common issues
• GUI
  • Starting the GUI
  • Using the GUI
  • Drawing masks
  • Bulk Mask Deletion
  • Training your own cellpose model
  • Keyboard shortcuts
• Inputs
• Settings
  • Channels
  • Diameter
  • Flow threshold
  • Cellprob threshold
  • Number of iterations niter
• Outputs
  • in a notebook
  • _seg.npy output
  • PNG output
  • Native ImageJ ROI archive output
  • (Legacy ImageJ Interface) ROI manager compatible output for ImageJ
  • Plotting functions
• 3D segmentation
  • Input format
  • Segmentation settings
  • Training for 3D segmentation
• Model
  • User-trained models
    • Finding models on BioImage.IO
    • Sharing models on BioImage.IO
• Image Restoration
  • DenoiseModel
  • CellposeDenoiseModel
  • Command line usage
  • Training your own models
• Training
  • CLI training options
  • Re-training a model
• Timing + memory usage
• Big Data
  • Distributed Cellpose for larger-than-memory data
  • Examples
• FAQ
  • Cellpose settings and usage
  • Models and training

Examples:

• In a notebook
• Command line
  • Command Line Usage

Reference:

• Cellpose API Guide
  • CellposeModel
    • CellposeModel
      • CellposeModel.diam_mean
      • CellposeModel.builtin
      • CellposeModel.device
      • CellposeModel.nclasses
      • CellposeModel.nbase
      • CellposeModel.net
      • CellposeModel.pretrained_model
      • CellposeModel.pretrained_model_ortho
      • CellposeModel.backbone
      • CellposeModel.__init__()
      • CellposeModel.eval()
      • CellposeModel.eval()
  • Training
    • train_seg()
  • Metrics
    • aggregated_jaccard_index()
    • average_precision()
    • boundary_scores()
    • mask_ious()
  • Flows <=> masks
    • compute_masks()
    • flow_error()
    • follow_flows()
    • get_masks_torch()
    • labels_to_flows()
    • masks_to_flows_gpu()
    • masks_to_flows_gpu_3d()
    • max_pool1d()
    • max_pool_nd()
    • remove_bad_flow_masks()
    • resize_and_compute_masks()
    • steps_interp()
  • Image transforms
    • average_tiles()
    • convert_image()
    • gaussian_kernel()
    • make_tiles()
    • move_axis()
    • move_min_dim()
    • normalize99()
    • normalize99_tile()
    • normalize_img()
    • pad_image_ND()
    • random_rotate_and_resize()
    • resize_image()
    • resize_safe()
    • smooth_sharpen_img()
    • unaugment_tiles()
    • update_axis()
  • I/O functions
    • add_model()
    • get_image_files()
    • get_label_files()
    • imread()
    • imread_2D()
    • imread_3D()
    • imsave()
    • load_images_labels()
    • load_train_test_data()
    • logger_setup()
    • masks_flows_to_seg()
    • remove_model()
    • save_masks()
    • save_rois()
    • save_to_png()
  • Utils functions
    • TqdmToLogger
      • TqdmToLogger.flush()
      • TqdmToLogger.write()
    • circleMask()
    • diameters()
    • dilate_masks()
    • distance_to_boundary()
    • download_url_to_file()
    • fill_holes_and_remove_small_masks()
    • get_mask_compactness()
    • get_mask_perimeters()
    • get_mask_stats()
    • get_masks_unet()
    • get_outline_multi()
    • get_perimeter()
    • masks_to_edges()
    • masks_to_outlines()
    • outlines_list()
    • outlines_list_multi()
    • outlines_list_single()
    • radius_distribution()
    • remove_edge_masks()
    • size_distribution()
    • stitch3D()
  • Plot functions
    • disk()
    • dx_to_circ()
    • image_to_rgb()
    • interesting_patch()
    • mask_overlay()
    • mask_rgb()
    • outline_view()
    • show_segmentation()
  • Transformer class
    • Transformer
      • Transformer.device
      • Transformer.dtype
      • Transformer.save_model()
  • Core functions
    • assign_device()
    • run_3D()
    • run_net()
    • use_gpu()
  • All models functions
    • CellposeModel
      • CellposeModel.diam_mean
      • CellposeModel.builtin
      • CellposeModel.device
      • CellposeModel.nclasses
      • CellposeModel.nbase
      • CellposeModel.net
      • CellposeModel.pretrained_model
      • CellposeModel.pretrained_model_ortho
      • CellposeModel.backbone
      • CellposeModel.__init__()
      • CellposeModel.eval()
      • CellposeModel.eval()
• Cellpose CLI
  • Command Line Usage
    • Named Arguments
    • Hardware Arguments
    • Input Image Arguments
    • Model Arguments
    • Algorithm Arguments
    • Output Arguments
    • Training Arguments