Efficient and Motion Correction-Free Myocardial Perfusion Segmentation in Small MRI Data Using Deep Transfer Learning From Cine Images: A Promising Framework for Clinical Implementation

Efficient and Motion Correction-Free Myocardial Perfusion Segmentation in Small MRI Data Using Deep Transfer Learning From Cine Images: A Promising Framework for Clinical Implementation

Perfusion cardiovascular magnetic resonance imaging is used to quantify the heart’s blood flow, which requires the segmentation of the myocardium, a laborious task. Deep learning-based methods, the most accurate to accomplish this task, still rely on expensive motion correction steps and...

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Bibliographic Details
Main Authors: German Garcia-Jara, Angel Jimenez-Molina, Esteban Reyes, Nicolas Tapia-Rivas, Cristobal Ramos-Gomez, Jose De Grazia, Matias Sepulveda
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Deep transfer learning
fine-tuning
myocardial perfusion
myocardial segmentation framework
U-net convolutional neural network
Online Access:https://ieeexplore.ieee.org/document/10247052/
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https://ieeexplore.ieee.org/document/10247052/

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