VesselFilling

This module uses a convolutionary neuronal network to fill empty tubes and vessels.

fill_vessels(source, sink, resample=None, threshold=0.5, network=None, dtype='float16', cuda=None, processing_parameter=None, verbose=False)

Fill hollow tubes via a neural network.

Arguments

sourcestr or Source

The binary data source to fill hollow tubes in.

sinkstr or Source.

The binary sink to write data to. sink is created if it does not exists.

resampleint or None

If int, downsample the data by this factor, apply network and upsample.

thresholdfloat or None

Apply a threshold to the result of the cnn. If None, the probability of being foreground is returned.

networkstr, Model or None

The network speicifcation. If None, the default trained network is used.

dtypestr

The dtype to use for the network. See ClearMap.ImageProcessing.MachineLearning.Torch.to() for details.

cudabool or None

If True, use gpu processing. If None, automatically detect gpu.

processing_parameterdict or None

Parameter to use for block processing.

verbosebool

If True, print progress.

Returns

networkModel

The neural network model.

train(network, data)

vessel_filling_network(network=None, dtype='float16', cuda=None)

Initialize vessel filling network.

Arguments

networkstr, Model or None

The network speicifcation. If None the default trained network is used.

dtypestr

The dtype to use for the network. See ClearMap.ImageProcessing.MachineLearning.Torch.to() for details.

cudabool or None

If True, use gpu processing. If None, automatically detect gpu.

Returns

networkModel

The neural network model.

network_binary_vessel_filling_filename = '/home/ckirst/Programs/ClearMap2/ClearMap/ImageProcessing/MachineLearning/VesselFilling/Resources/cnn_binary_vessel_filling.pth'

Filename of the default neuronal network to use for binary hollow vessel filling.

resources_path = '/home/ckirst/Programs/ClearMap2/ClearMap/ImageProcessing/MachineLearning/VesselFilling/Resources'

Path to the trained neuronal networks.