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Titel:

Combining generative modelling and semi-supervised domain adaptation for whole heart cardiovascular magnetic resonance angiography segmentation.

Dokumenttyp:
Journal Article; Research Support, Non-U.S. Gov't
Autor(en):
Muffoletto, Marica; Xu, Hao; Kunze, Karl P; Neji, Radhouene; Botnar, René; Prieto, Claudia; Rückert, Daniel; Young, Alistair A
Abstract:
BACKGROUND: Quantification of three-dimensional (3D) cardiac anatomy is important for the evaluation of cardiovascular diseases. Changes in anatomy are indicative of remodeling processes as the heart tissue adapts to disease. Although robust segmentation methods exist for computed tomography angiography (CTA), few methods exist for whole-heart cardiovascular magnetic resonance angiograms (CMRA) which are more challenging due to variable contrast, lower signal to noise ratio and a limited amount...     »
Zeitschriftentitel:
J Cardiovasc Magn Reson
Jahr:
2023
Band / Volume:
25
Heft / Issue:
1
Volltext / DOI:
doi:10.1186/s12968-023-00981-6
PubMed:
http://view.ncbi.nlm.nih.gov/pubmed/38124106
Print-ISSN:
1097-6647
TUM Einrichtung:
Institut für KI und Informatik in der Medizin (Prof. Rückert)
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