Knee menisci segmentation and relaxometry of 3D ultrashort echo time cones MR imaging using attention U-Net with transfer learning.

PurposeTo 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, T1ρ , and T2∗ parameters, which can be used to assess knee osteoarthritis (OA).MethodsWhole 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 T1ρ -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, T1ρ , T2∗ relaxations, and ROI areas were determined for the manual and automatic segmentations, then compared.ResultsThe 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, T1ρ , and T2∗ 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.ConclusionThe 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

Automated segmentation of the menisci from MR images

Pathologic processes active in early-stage knee joint osteoarthritis may also affect the integrity of the crescent-shaped fibrocartilagenous structures called menisci. Magnetic resonance imaging can allow the detection of these structural changes, however, large-scale clinical application remains limited by tedious and labor-intensive techniques for volumetric measurement. Towards automating these quantitative measurements, we have currently developed a scheme that allows the automatic segmentation of the menisci from MR images of healthy knees. This scheme utilizes prior automatic bone and cartilage segmentations to provide spatial localization, before shape model fitting and tissue classification are used to segment the menisci. The accuracy and robustness of the approach was experimentally validated using a set of 14 fat suppressed Spoiled Gradient Recall MR images. An average Dice Similarity Coefficient of 0.75 and 0.77 was obtained for the medial and lateral meniscus, illustrating the accuracy of the approach, while the coefficient of variation for volume was 2.29 and 1.50, respectively