zea.models.carotid_segmenter

Carotid segmentation model.

To try this model, simply load one of the available presets:

>>> from zea.models.carotid_segmenter import CarotidSegmenter

>>> model = CarotidSegmenter.from_preset("carotid-segmenter")

Important

This is a zea implementation of the model. For the original paper see:

van Knippenberg, Luuk, et al. “Unsupervised domain adaptation method for segmenting cross-sectional CCA images.” https://doi.org/10.1016/j.cmpb.2022.107037

See also

A tutorial notebook where this model is used: Carotid artery segmentation.

Classes

CarotidSegmenter(*args, **kwargs)

Carotid segmentation model.

class zea.models.carotid_segmenter.CarotidSegmenter(*args, **kwargs)

Bases: BaseModel

Carotid segmentation model.

Initializes the carotid segmenter model.

Based on U-Net architecture.

Original implementation of paper:

• “Unsupervised domain adaptation method for segmenting cross-sectional CCA images”

• https://doi.org/10.1016/j.cmpb.2022.107037

call(inputs)

Segment the input image.

get_config()

Returns the config of the object.

An object config is a Python dictionary (serializable) containing the information needed to re-instantiate it.