Generalizability of Deep Learning Segmentation Algorithms for Automated Assessment of Cartilage Morphology and MRI Relaxometry

Background Deep learning (DL)‐based automatic segmentation models can expedite manual segmentation yet require resource‐intensive fine‐tuning before deployment on new datasets. The generalizability of DL methods to new datasets without fine‐tuning is not well characterized. Purpose Evaluate the gene...

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Bibliographic Details
Published in:	Journal of magnetic resonance imaging Vol. 57; no. 4; pp. 1029 - 1039
Main Authors:	Schmidt, Andrew M., Desai, Arjun D., Watkins, Lauren E., Crowder, Hollis A., Black, Marianne S., Mazzoli, Valentina, Rubin, Elka B., Lu, Quin, MacKay, James W., Boutin, Robert D., Kogan, Feliks, Gold, Garry E., Hargreaves, Brian A., Chaudhari, Akshay S.
Format:	Journal Article
Language:	English
Published:	Hoboken, USA John Wiley & Sons, Inc 01.04.2023 Wiley Subscription Services, Inc
Subjects:	Accuracy Algorithms Arthritis Cartilage Cartilage diseases Cartilage, Articular - pathology Coefficient of variation Correlation coefficient Correlation coefficients Data acquisition Datasets Deep Learning Female Females Field strength Humans Knee Machine learning Magnetic resonance imaging Magnetic Resonance Imaging - methods Mathematical models Osteoarthritis Osteoarthritis, Knee - pathology Parameters Population studies qMRI Retrospective Studies Scanners Segmentation Statistical analysis Statistical tests Tuning Variation segmentation machine learning osteoarthritis cartilage qMRI
ISSN:	1053-1807, 1522-2586, 1522-2586
Online Access:	Get full text
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