Segmentation Module
Segmentation [Cellpose]
Segmentation Module currently utilizes the Cellpose model only (more to come soon), a generalist algorithm for cell segmentation, to process volumetric datasets. It provides tools to perform and visualize 3D segmentation on microscopy images, facilitating the analysis of biological structures within a 3D space.
- exm.segmentation.segmentation.display_3d_masks(image, masks)[source]
Displays the 3D masks on the given image.
- Parameters:
image (numpy.ndarray) – The image to display.
masks (numpy.ndarray) – The masks to overlay on the image.
- exm.segmentation.segmentation.segment_3d(volume, model, downsample=False, chan=0, chan2=0, diameter=30, flow_threshold=0.4, cellprob_threshold=0, do_3D=True)[source]
Performs 3D segmentation on the given volume using the provided model.
- Parameters:
volume (numpy.ndarray) – The volume to segment.
model (cellpose.models.CellposeModel) – The model to use for segmentation.
downsample (bool) – Whether to downsample the volume before segmentation. Default is False.
chan (int) – The channel to use for segmentation. Default is 0.
chan2 (int) – The second channel to use for segmentation. Default is 0.
diameter (float) – Estimated diameter of objects to segment. Set to 0 to auto-estimate. Default is 30.
flow_threshold (float) – The flow threshold for the segmentation. Default is 0.4.
cellprob_threshold (float) – The cell probability threshold for the segmentation. Default is 0.
do_3D (bool) – Whether to perform 3D segmentation. Default is True.
- Returns:
The segmented masks.
- Return type: