Deep CNNs with Physical Constraints for simultaneous Multi-tissue Segmentation and Multi-parameter Quantification (MSMQ-Net) of Knee
Xing Lu1, Yajun Ma1, Kody Xu1, Saeed Jerban1, Hyungseok Jang1, Chun-Nan Hsu2, Amilcare Gentili1,3, Eric Y Chang1,3, and Jiang Du1
1Department of Radiology, University of California, San Diego, San Diego, CA, United States, 2Department of Neurosciences, University of California, San Diego, San Diego, CA, United States, 3Radiology Service, Veterans Affairs San Diego Healthcare System, San Diego, CA, United States
In this study, we proposed end-to-end deep learning convolutional neural networks to perform simultaneous multi-tissue segmentation and multi-parameter quantification (MSMQ-Net) on the knee without and with physical constraints. The performance robustness of MSMQ-Net was also evaluated using reduced input magnetic resonance images. Results demonstrated the potential of MSMQ-Net for fast and accurate UTE-MRI analysis of the knee, a “whole-organ” approach which is impossible with conventional clinical MRI.
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