Improved robustness for deep learning-based segmentation of multi-center myocardial perfusion cardiovascular MRI datasets using data-adaptive uncertainty–guided space-time analysis

ABSTRACT: Background: Fully automatic analysis of myocardial perfusion cardiovascular magnetic resonance imaging datasets enables rapid and objective reporting of stress/rest studies in patients with suspected ischemic heart disease. Developing deep learning techniques that can analyze multi-center...

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Bibliographic Details
Main Authors:	Dilek M. Yalcinkaya, MS, Khalid Youssef, PhD, Bobak Heydari, MD, Janet Wei, MD, C. Noel Bairey Merz, MD, Robert Judd, PhD, Rohan Dharmakumar, PhD, Orlando P. Simonetti, PhD, Jonathan W. Weinsaft, MD, Subha V. Raman, MD, Behzad Sharif, PhD
Format:	Article
Language:	English
Published:	Elsevier 2024-01-01
Series:	Journal of Cardiovascular Magnetic Resonance
Subjects:	Myocardial perfusion Stress perfusion CMR Artificial intelligence Deep learning Ischemic heart disease Multi-center
Online Access:	http://www.sciencedirect.com/science/article/pii/S1097664724011098
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Improved robustness for deep learning-based segmentation of multi-center myocardial perfusion cardiovascular MRI datasets using data-adaptive uncertainty–guided space-time analysis

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