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Knee menisci segmentation and relaxometry of 3D ultrashort echo time cones MR imaging using attention U‐Net with transfer learning
Author(s) -
Byra Michal,
Wu Mei,
Zhang Xiaodong,
Jang Hyungseok,
Ma YaJun,
Chang Eric Y.,
Shah Sameer,
Du Jiang
Publication year - 2020
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.27969
Subject(s) - segmentation , artificial intelligence , deep learning , relaxometry , computer science , convolutional neural network , magnetic resonance imaging , dice , pattern recognition (psychology) , osteoarthritis , transfer of learning , nuclear medicine , medicine , radiology , mathematics , spin echo , pathology , geometry , alternative medicine
Purpose To develop a deep learning‐based method for knee menisci segmentation in 3D ultrashort echo time (UTE) cones MR imaging, and to automatically determine MR relaxation times, namely the T1, T1 ρ , and T 2 ∗ parameters, which can be used to assess knee osteoarthritis (OA). Methods Whole knee joint imaging was performed using 3D UTE cones sequences to collect data from 61 human subjects. Regions of interest (ROIs) were outlined by 2 experienced radiologists based on subtracted T1 ρ ‐weighted MR images. Transfer learning was applied to develop 2D attention U‐Net convolutional neural networks for the menisci segmentation based on each radiologist's ROIs separately. Dice scores were calculated to assess segmentation performance. Next, the T1, T1 ρ , T 2 ∗ relaxations, and ROI areas were determined for the manual and automatic segmentations, then compared. Results The models developed using ROIs provided by 2 radiologists achieved high Dice scores of 0.860 and 0.833, while the radiologists’ manual segmentations achieved a Dice score of 0.820. Linear correlation coefficients for the T1, T1 ρ , and T 2 ∗ relaxations calculated using the automatic and manual segmentations ranged between 0.90 and 0.97, and there were no associated differences between the estimated average meniscal relaxation parameters. The deep learning models achieved segmentation performance equivalent to the inter‐observer variability of 2 radiologists. Conclusion The proposed deep learning‐based approach can be used to efficiently generate automatic segmentations and determine meniscal relaxations times. The method has the potential to help radiologists with the assessment of meniscal diseases, such as OA.