Learning 3D aortic root assessment based on sparse annotations

Johanna Brosig; Nina Krüger; Isaac Wamala; Matthias Ivantsits; Simon Sündermann; Jörg Kempfert; Stefan Heldmann; Anja Hennemuth

doi:10.1117/12.3006445

3 April 2024 Learning 3D aortic root assessment based on sparse annotations

Johanna Brosig, Nina Krüger, Isaac Wamala, Matthias Ivantsits, Simon Sündermann, Jörg Kempfert, Stefan Heldmann, Anja Hennemuth

Author Affiliations +

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 129271S (2024) https://doi.org/10.1117/12.3006445
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Analyzing the anatomy of the aorta and left ventricular outflow tract (LVOT) is crucial for risk assessment and planning of transcatheter aortic valve implantation (TAVI). We propose 2D cross-sectional annotation and point cloud-based surface reconstruction to train a fully automatic 3D segmentation network for the aortic root and the LVOT. Our sparse annotation scheme enables easy and fast training data generation for tubular structures like the aortic root. Based on this annotation concept, we trained a 3D segmentation model that achieves a Dice similarity coefficient (DSC) of 0.9 and an average surface distance (ASD) of 0.96 mm. In addition, we show that our fully automatic segmentation approach facilitates reproducible and quantifiable measurements for TAVI planning. Our approach achieves an aortic annulus maximum diameter difference between prediction and annotation of 0.45 mm (inter-observer variance: 0.25 mm).

Citation Download Citation

Johanna Brosig, Nina Krüger, Isaac Wamala, Matthias Ivantsits, Simon Sündermann, Jörg Kempfert, Stefan Heldmann, and Anja Hennemuth "Learning 3D aortic root assessment based on sparse annotations", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 129271S (3 April 2024); https://doi.org/10.1117/12.3006445

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