zea.models.echonet

Echonet-Dynamic segmentation model for cardiac ultrasound segmentation.

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

>>> from zea.models.echonet import EchoNetDynamic

>>> model = EchoNetDynamic.from_preset("echonet-dynamic")

Important

This is a zea implementation of the model. For the original paper and code, see here.

Ouyang, David, et al. “Video-based AI for beat-to-beat assessment of cardiac function.” Nature 580.7802 (2020): 252-256

See also

A tutorial notebook where this model is used: Left ventricle segmentation.

Classes

EchoNetDynamic(*args, **kwargs)

EchoNet-Dynamic segmentation model for cardiac ultrasound segmentation.
class zea.models.echonet.EchoNetDynamic(*args, **kwargs)[source]

Bases: BaseModel

EchoNet-Dynamic segmentation model for cardiac ultrasound segmentation.

Preprocessing should normalize the input images with mean and standard deviation.

build(input_shape)[source]

Builds the network.

call(inputs)[source]

Segment the input image.

custom_load_weights(preset, **kwargs)[source]

Load the weights for the segmentation model.

maybe_convert_to_jax(input_shape)[source]

Converts the network to Jax if backend is Jax.