Improving annotation accuracy in MRI data using MR Fingerprinting and deep learning
Yong Chen1, Rasim Boyacioglu1, Gamage Sugandima Nishadi Weragoda2, Michael Martens2, Chaitra Badve1,3, and Mark Griswold1
1Radiology, Case Western Reserve University, Cleveland, OH, United States, 2Physics, Case Western Reserve University, Cleveland, OH, United States, 3Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, United States
In this study, we introduced a new deep learning method to take advantage of both radiologists’ expertise and multi-parametric MR Fingerprinting data to improve annotation accuracy in MRI dataset. A U-Net based convolutional neural network was adopted and each dataset was evaluated multiple times using different combinations of training dataset. Our initial results obtained from a brain tumor dataset demonstrates that the developed method could effectively identify mislabeled tissues and improve annotation accuracy.
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