ukbb_cardiac segmentation network

Imperial College London (Bai et al.)

Cardiac MRI

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Cardiac MRI

Cardiac chamber segmentation

Filter catalog by Disease / Trait:
Structural Heart & Cardiomyopathy

Segmentation

Filter catalog by Task Type:
Segmentation & Detection

CNN (2D)

Filter catalog by Architecture:
Convolutional (CNN)

TensorFlow

Filter catalog by Framework:
TensorFlow / Keras

Apache 2.0

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Permissive

file_copy

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Long-standing toolbox for automated segmentation of the ventricles and atria and derivation of cardiac imaging phenotypes from short- and long-axis cine cardiac MRI. Built on a fully convolutional network trained per slice, and widely reused across UK Biobank cardiac imaging studies since its 2018 publication. Developed at Imperial College London.

memory Specifications

gavel License

Apache 2.0

check_small Open source check_small Commercial use OK check_small Modifications allowed Attribution required No share-alike requirement

License for model weights only. Associated code may be licensed seperately, check code source for specific terms.

description Publication

Automated cardiovascular magnetic resonance image analysis with fully convolutional networks open_in_new

2018 · original paper

DOI: 10.1186/s12968-018-0471-x

database Training & evaluation data

UK Biobank Cardiac MRI open_in_new

train

public 74,916 subjects · United Kingdom

UK Biobank cardiac MRI imaging substudy; CineMA pretrained on 74,916 cine CMR studies, ukbb_cardiac (Bai et al. 2018) trained on ~4,875 subjects / 93,500 annotated images from an earlier release.

science Capabilities & performance