---
license: bsd-3-clause
license_name: bsd-3-clause-code-cc-by-nc-data
library_name: onnx
tags:
  - cellpose
  - cell-segmentation
  - microscopy
  - onnx
  - biology
pipeline_tag: image-segmentation
---

# Cellpose cpsam — ONNX

ONNX export of the [Cellpose](https://github.com/MouseLand/cellpose) **cpsam** (Cellpose-SAM) model for cell segmentation in microscopy images.

## Model Details

- **Architecture**: ViT-L based Transformer (SAM backbone + Cellpose readout)
- **Parameters**: 304M
- **Input**: `pixel_values` — float32 tensor `(1, 3, 256, 256)` (one tile)
- **Output**: `output` — float32 tensor `(1, 3, 256, 256)` — channels are `(dY, dX, cellprob)`
- **Opset**: 14

The model weights are stored as external data (`model.onnx.data`) due to the 2GB protobuf limit.
Both files must be in the same directory for inference.

## Usage

### Python (onnxruntime)
```python
import onnxruntime as ort
import numpy as np

sess = ort.InferenceSession("model.onnx", providers=["CUDAExecutionProvider"])
tile = np.zeros((1, 3, 256, 256), dtype=np.float32)  # your preprocessed tile
output = sess.run(None, {"pixel_values": tile})[0]    # (1, 3, 256, 256)
dY, dX, cellprob = output[0, 0], output[0, 1], output[0, 2]
```

### Rust (cellpose-rs)
```rust
use cellpose_rs::{CellposeSession, SegmentParams, preprocess};

let mut session = CellposeSession::new(Path::new("model.onnx"), false)?;
let chw = preprocess::build_chw_image(phase, fluo, h, w);
let masks = session.segment(&chw, h, w, SegmentParams::default())?;
```

See [cellpose-rs](https://github.com/keejkrej/cellpose-rs) for the full Rust inference library.

## Post-processing

The raw output requires Cellpose flow dynamics post-processing:
1. Threshold `cellprob > 0` to get foreground
2. Scale flows: `dP = dP * foreground / 5.0`
3. 200-step Euler integration with bilinear interpolation
4. Histogram-based seed detection (max-pool kernel=5, threshold=10)
5. Connected-component labelling of seeds
6. Assign each foreground pixel to its nearest seed
7. Remove masks >40% of image area, fill holes, remove masks <15px

## License

The Cellpose **code** is © 2020 Howard Hughes Medical Institute, licensed under the
[BSD 3-Clause License](https://github.com/MouseLand/cellpose/blob/main/LICENSE).

⚠️ The Cellpose-SAM model is trained on data licensed under **CC-BY-NC**.
See the [upstream notice](https://github.com/MouseLand/cellpose#readme).

## Citation

If you use this model, please cite the Cellpose-SAM paper:

> Pachitariu, M., Rariden, M., & Stringer, C. (2025).
> Cellpose-SAM: superhuman generalization for cellular segmentation.
> *bioRxiv*. https://doi.org/10.1101/2025.04.28.651001

```bibtex
@article{pachitariu2025cellposesam,
  title={Cellpose-SAM: superhuman generalization for cellular segmentation},
  author={Pachitariu, Marius and Rariden, Maia and Stringer, Carsen},
  journal={bioRxiv},
  year={2025},
  doi={10.1101/2025.04.28.651001}
}
```