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Abstract #0332

Automatic Segmentation of the Great Arteries for Robust Hemodynamic Assessment

Javier Montalt-Tordera1, Endrit Pajaziti1, Rod Jones2, Jennifer Steeden1, Silvia Schievano1, and Vivek Muthurangu1
1University College London, London, United Kingdom, 2Great Ormond Street Hospital, London, United Kingdom


Computational fluid dynamics (CFD) are useful in the assessment of blood flow conditions in patients with congenital heart disease. A necessary, time-consuming step in the creation of CFD models is the segmentation of the anatomy of interest. In this work, a neural network was trained to segment the aorta and the pulmonary arteries in 3D MRI, and its performance was evaluated in the context of a CFD application. The network performs well in terms of Dice score and is shown to lead to accurate pressure and flow velocity fields, with errors at the level of inter-observer variability.

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