Segmentation

Main segmentation workflow using Cellpose-SAM (or Cellpose 4):

This function reads the preprocessed image, loads the Cellpose cyto3 model (on GPU if available), and then runs segmentation (using model.eval) to produce three main outputs:

• Masks – integer-labeled segmentation maps

• Flows – vector fields showing pixel movement toward cell centers

• Styles – latent style vectors describing image-level features

Key parameters that influence segmentation include:

• flow_threshold=1 – controls the confidence threshold for detecting cells.

• cellprob_threshold=-3 – sets the probability threshold for cell detection.

• diameter=None – lets cellpose automatically estimates cell diameter.

For more details on these outputs, see Outputs.

   def run_cellpose(
    self,
    img_path: str,
    model_type: str = "cyto3",
    gpu: bool = True,
    channels: list[int] = [0, 0],
    diameter: float | None = None,
    flow_threshold: float = 1,
    cellprob_threshold: float = -3,
):
    """Run Cellpose-SAM segmentation on the preprocessed image."""
    print(img_path)
    img = io.imread(img_path)
    model = models.CellposeModel(model_type=model_type, gpu=gpu)

    self.masks, self.flows, self.styles = model.eval(
        img,
        diameter=diameter,
        channels=channels,
        flow_threshold=flow_threshold,
        cellprob_threshold=cellprob_threshold,
    )

    self.clear_gpu_memory()
    return self.masks, self.flows, self.styles