deepcell_types.predict

deepcell_types.predict(raw, mask, channel_names, mpp, model_name, device_num, batch_size=256, num_workers=24, tissue_exclude=None)

Run the cell-type prediction pipeline.

Given a spatial proteomics image raw, a corresponding segmentation mask, and a list of markers (channel_names) corresponding to the channels of raw, predict the cell type associated with each index in mask.

Parameters:

rawA spatial proteomic image as an numpy.ndarray with shape (C, W, H).

A 2D multiplexed image in channel-first format. The image will be converted internally to dtype=np.float32.

mask2D label image

Segmentation mask of raw as a 2D label image with shape (W, H).

channel_nameslist of str

A list of channel markers. Must have the same length as the number of channels in raw and be given in the same order as the channels in raw.

mppfloat

The image resolution in microns-per-pixel. Improves prediction performance by removing scale variability.

model_namestr

Name of the pre-trained model to use for inference. Models are searched for at Path.home() / ".deepcell/models".

device_numtorch.device or str

Which device to run inference on. For example, "cpu" or "cuda". To specify a specific GPU on multi-GPU systems, try "cuda:<device_num>, e.g. "cuda:0".

batch_sizeint, default=256

Batch size to be used for inference. Larger batch_size will increase performance by increasing VRAM usage. Default value of 256 is conservative and should be appropriate for systems with <16GB VRAM.

num_workersint, default=24

Number of threads to use for loading data. Increasing num_workers may result in large increases in CPU memory footprint. Only recommended for systems with >64 GB RAM.

tissue_excludestr, optional, default=None

If provided, limit the cell type prediction to only those categories known to be associated with the specified tissue type.

Returns:

list of str

A list whose len is equal to the number of unique cell indices in mask, ordered by ascending cell index.