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.

Reference

L. van Knippenberg, R. J. G. van Sloun, M. Mischi, J. de Ruijter, R. Lopata, and R. A. Bouwman, “Unsupervised Domain Adaptation Method for Segmenting Cross-Sectional CCA Images,” Computer Methods and Programs in Biomedicine, vol. 225, p. 107037, 2022. 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.

Reference

L. van Knippenberg, R. J. G. van Sloun, M. Mischi, J. de Ruijter, R. Lopata, and R. A. Bouwman, “Unsupervised Domain Adaptation Method for Segmenting Cross-Sectional CCA Images,” Computer Methods and Programs in Biomedicine, vol. 225, p. 107037, 2022. 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.