Cellpose

Cellpose is a deep learning model for cell segmentation. It detects and outlines individual cells in microscopy images, supporting multiple cell types and automatic diameter estimation.

Input formats

.png, .jpg, .jpeg, .tiff, .bmp, .webp

Model variants

Variant	Description
cyto3 (default)	Latest cytoplasm model — best general-purpose accuracy
cyto2	Second-generation cytoplasm model
cyto	Original cytoplasm model
nuclei	Nuclei-only detection

Parameters

Parameter	Range	Default	Description
Model type	cyto/cyto2/cyto3/nuclei	cyto3	Which Cellpose model to use
Diameter	0–500 px	0 (auto)	Expected cell diameter. Set to 0 for auto-detection.
Flow threshold	0–1	0.4	Maximum flow error per mask (advanced)
Cell probability threshold	-6 to +6	0.0	Cell probability cutoff (advanced)
Channels	[int, int]	[0, 0]	Cytoplasm and nucleus channel indices (advanced)

Outputs

Output	Format	Description
Segmentation mask	PNG	Combined instance segmentation mask
Polygons	JSON (GeoJSON)	Vector outlines of each detected cell

Presets

Preset	Description
Cell Segmentation	cyto3 model with auto-diameter
Nuclei Only	nuclei model for nuclear detection

Compute requirements

Resource	Requirement
GPU	Required — min 8 GB VRAM, 16 GB recommended
Duration	~30 seconds per image

SAM3 — Segment Anything

DeepLabCut — Pose Estimation