507-04-007 ISMRM Abstract

Deep Learning 4D Segmentation of Short-Axis CMR to Quantify Peak Filling and Ejection Rate in Single Ventricle Patients

Primary: Pediatrics - Cardiovascular

Secondary: Analysis Methods - Segmentation and Detection

507-04-007 · Advanced Segmentation in MRI Across Scales and Domains · Wednesday, 13 May, 4:00 PM–5:50 PM · Meeting Room 1.40

Keywords: Cardiac MRI Ventricular function Deep Learning Segmentation Congenital Heart Disease

Accepted

Tina Yao ¹, Nicole St. Clair², Gabriel F Miller², Jennifer Steeden¹, Rahul H Rathod², Vivek Muthurangu¹

¹University College London, London, United Kingdom

²Boston Children's Hospital, Boston, United States of America

Presenting Author: Tina Yao

Synopsis

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References

1. Fontan. Force Study Fontan Outcomes Registry Using Cmr Examinations [Internet]. 2023. Available from: https://www.forceregistry.org

2. Yao T, Clair NS, Miller GF, Dorfman AL, Fogel MA, Ghelani S, et al. A Deep Learning Pipeline for Assessing Ventricular Volumes from a Cardiac MRI Registry of Patients with Single Ventricle Physiology. Radiol Artif Intell. 2024. doi:10.1148/ryai.230132 [doi]

3. Huang H, Lin L, Tong R, Hu H, Zhang Q, Iwamoto Y, et al. UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation . arXiv; 2020 [cited 2023 Feb 14] [Internet]. 2020. Available from: http://arxiv.org/abs/2004.0879

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/507-04-007