Multi-step segmentation

Multi-step segmentation consists of running multiple times Cellpose over the whole slides with different parameters. For instance, we can first run a nucleus segmentation using DAPI, then another round using DAPI and a membrane staining, and finally, DAPI and cell boundary staining. This can make the segmentation more robust. Note that the results of the multiple steps are combined into one final segmentation.

Using the API

To run multi-step segmentation via the API, you can run multiple segmentation methods, and then combine them with sopa.segmentation.combine.

On the example below, we run Cellpose twice, once for nuclei and once for tumor cells. We then combine the two segmentations into a single one.

import sopa

sdata = sopa.io.toy_dataset(length=1000)
sopa.make_image_patches(sdata)

sopa.segmentation.cellpose(sdata, "DAPI", diameter=35, key_added="nuclei")
sopa.segmentation.cellpose(sdata, ["DAPI", "CK"], diameter=35, key_added="tumor_cells")

sopa.segmentation.combine(sdata, ["nuclei", "tumor_cells"], key_added="combined_cells")

Then, you can continue as usual, i.e. with sopa.aggregate and so on.

Using the CLI

Warning

Here, we only detail the multi-step segmentation. For the rest of the CLI usage, refer to our CLI usage tutorial, and only replace the "Run segmentation" section with the instructions below.

First, generate the bounding boxes of the patches on which Cellpose will be run. Here, the patches have a width and height of 1500 pixels, and an overlap of 50 pixels. We advise bigger sizes for real datasets (see our default parameters in one of our config files). On the toy dataset, this will generate 4 patches.

sopa patchify image tuto.zarr --patch-width-pixel 1500 --patch-overlap-pixel 50

Now, we can run Cellpose on each of the four patches and for each "segmentation step" we want. In this toy example, we run 3 steps with (i) DAPI + CK, (ii) DAPI + CD3, and (iii) DAPI + CD20.

sopa segmentation cellpose tuto.zarr \
    --channels DAPI --channels CK \
    --cache-dir-name cellpose_CK \
    --diameter 35 \
    --min-area 2000

sopa segmentation cellpose tuto.zarr \
    --channels DAPI --channels CD3 \
    --cache-dir-name cellpose_CD3 \
    --diameter 35 \
    --min-area 2000

sopa segmentation cellpose tuto.zarr \
    --channels DAPI --channels CD20 \
    --cache-dir-name cellpose_CD20 \
    --diameter 35 \
    --min-area 2000

Note

In the above commands, the --diameter and --min-area parameters are specific to the data type we work on. For your own data, consider using the default parameters from one of our config files. Here, min-area is in pixels^2.

At this stage, you executed 12 times Cellpose (3 steps on each of the 4 patches). Now, we need to resolve the conflict, i.e., merge the three segmentations into one. Note that we gave the paths to the temporary boundaries we made above.

sopa resolve cellpose tuto.zarr \
    --cache-dir-name cellpose_CK \
    --cache-dir-name cellpose_CD3 \
    --cache-dir-name cellpose_CD20

Congrats, you have now merged the results of a three-step segmentation! You can now refer to our normal CLI usage tutorial for all the other tasks.