Bulletin of the American Physical Society
APS March Meeting 2024
Monday–Friday, March 4–8, 2024; Minneapolis & Virtual
Session GG03: V: 1st Joint National Centre for Physics & APS March Meeting Satellite Session
5:30 AM–7:30 AM,
Wednesday, March 6, 2024
Room: Virtual Room 03
Chair: Jie Yang
Abstract: GG03.00003 : Four Chamber Whole Heart Segmentation and Reconstruction for evaluating healthy and non-healthy Heart state based on Deep Learning models
6:50 AM–7:30 AM
Presenter:
Abdul Qayyum
(National Heart and Lung Institute)
Author:
Abdul Qayyum
(National Heart and Lung Institute)
The 3D shape of the atria and ventricles is important for studying the mechanisms of disease processes. Left atrial volume is commonly estimated using the bi-plane area-length method from two-chamber (2CH) and four-chamber (4CH) long axes views. However, this can be inaccurate due to a violation of geometric assumptions. We aimed to develop a deep learning neural network to infer 3D left atrial shape, volume, and surface area from 2CH and 4CH views.
In this abstract, we have proposed Attention-guided Generative adversarial and efficient 3D volumetric probabilistic diffusion deep learning models for 4CH whole heart segmentation and reconstruction using private non-annotated clinical MRI and open source annotated MICCAI challenge datasets. The MICCAI dataset has abandoned annotation while our private clinical MRI dataset has no manual annotation. We first generated the target clinical MRI data from unpaired MICCAI challenges datasets using Attention-guided Generative adversarial network and transform the style or miss alignment pixels from unpaired source MICCAI annotated datasets to our private non-annotated dataset and then segment left ventricle (LV), right ventricle (RV), left arterial (RA), and right atrial (RA) using 3D volumetric probabilistic diffusion model. Further A 3D UNet was trained and tested using 2CH and 4CH segmentations generated from 3D coronary computed tomography angiography (CCTA) segmentations. The sparse input label map volume was converted to a dense label map by the label completion network, giving dense volumetric label maps of the LA, LV, left/right pulmonary veins. Our proposed model achieved better performance using the generated clinical MRI without annotated labels. This process aids in analyzing myocardium function and conducting biomechanical analyses from imaging data.
Follow Us |
Engage
Become an APS Member |
My APS
Renew Membership |
Information for |
About APSThe American Physical Society (APS) is a non-profit membership organization working to advance the knowledge of physics. |
© 2024 American Physical Society
| All rights reserved | Terms of Use
| Contact Us
Headquarters
1 Physics Ellipse, College Park, MD 20740-3844
(301) 209-3200
Editorial Office
100 Motor Pkwy, Suite 110, Hauppauge, NY 11788
(631) 591-4000
Office of Public Affairs
529 14th St NW, Suite 1050, Washington, D.C. 20045-2001
(202) 662-8700