Three-dimensional MRI Bone Models of the Glenohumeral Joint Using Deep Learning: Evaluation of Normal Anatomy and Glenoid Bone Loss.

Autor:	Cantarelli Rodrigues T; Department of Radiology, Hospital do Coração (HCOR) and Teleimagem, Rua Desembargador Eliseu Guilherme 53, 7th Floor, São Paulo, SP, Brazil 04004-030 (T.C.R.); Department of Radiology, NYU Langone Medical Center, New York, NY (C.M.D., E.F.A., S.G.); Department of Radiology, McGill University Health Centre, Montreal, Canada (N.G.); and Department of Radiology, New York University School of Medicine, New York, NY (J.S.B., J.D.)., Deniz CM; Department of Radiology, Hospital do Coração (HCOR) and Teleimagem, Rua Desembargador Eliseu Guilherme 53, 7th Floor, São Paulo, SP, Brazil 04004-030 (T.C.R.); Department of Radiology, NYU Langone Medical Center, New York, NY (C.M.D., E.F.A., S.G.); Department of Radiology, McGill University Health Centre, Montreal, Canada (N.G.); and Department of Radiology, New York University School of Medicine, New York, NY (J.S.B., J.D.)., Alaia EF; Department of Radiology, Hospital do Coração (HCOR) and Teleimagem, Rua Desembargador Eliseu Guilherme 53, 7th Floor, São Paulo, SP, Brazil 04004-030 (T.C.R.); Department of Radiology, NYU Langone Medical Center, New York, NY (C.M.D., E.F.A., S.G.); Department of Radiology, McGill University Health Centre, Montreal, Canada (N.G.); and Department of Radiology, New York University School of Medicine, New York, NY (J.S.B., J.D.)., Gorelik N; Department of Radiology, Hospital do Coração (HCOR) and Teleimagem, Rua Desembargador Eliseu Guilherme 53, 7th Floor, São Paulo, SP, Brazil 04004-030 (T.C.R.); Department of Radiology, NYU Langone Medical Center, New York, NY (C.M.D., E.F.A., S.G.); Department of Radiology, McGill University Health Centre, Montreal, Canada (N.G.); and Department of Radiology, New York University School of Medicine, New York, NY (J.S.B., J.D.)., Babb JS; Department of Radiology, Hospital do Coração (HCOR) and Teleimagem, Rua Desembargador Eliseu Guilherme 53, 7th Floor, São Paulo, SP, Brazil 04004-030 (T.C.R.); Department of Radiology, NYU Langone Medical Center, New York, NY (C.M.D., E.F.A., S.G.); Department of Radiology, McGill University Health Centre, Montreal, Canada (N.G.); and Department of Radiology, New York University School of Medicine, New York, NY (J.S.B., J.D.)., Dublin J; Department of Radiology, Hospital do Coração (HCOR) and Teleimagem, Rua Desembargador Eliseu Guilherme 53, 7th Floor, São Paulo, SP, Brazil 04004-030 (T.C.R.); Department of Radiology, NYU Langone Medical Center, New York, NY (C.M.D., E.F.A., S.G.); Department of Radiology, McGill University Health Centre, Montreal, Canada (N.G.); and Department of Radiology, New York University School of Medicine, New York, NY (J.S.B., J.D.)., Gyftopoulos S; Department of Radiology, Hospital do Coração (HCOR) and Teleimagem, Rua Desembargador Eliseu Guilherme 53, 7th Floor, São Paulo, SP, Brazil 04004-030 (T.C.R.); Department of Radiology, NYU Langone Medical Center, New York, NY (C.M.D., E.F.A., S.G.); Department of Radiology, McGill University Health Centre, Montreal, Canada (N.G.); and Department of Radiology, New York University School of Medicine, New York, NY (J.S.B., J.D.).
Jazyk:	angličtina
Zdroj:	Radiology. Artificial intelligence [Radiol Artif Intell] 2020 Sep 09; Vol. 2 (5), pp. e190116. Date of Electronic Publication: 2020 Sep 09.
DOI:	10.1148/ryai.2020190116
Abstrakt:	Purpose: To use convolutional neural networks (CNNs) for fully automated MRI segmentation of the glenohumeral joint and evaluate the accuracy of three-dimensional (3D) MRI models created with this method. Materials and Methods: Shoulder MR images of 100 patients (average age, 44 years; range, 14-80 years; 60 men) were retrospectively collected from September 2013 to August 2018. CNNs were used to develop a fully automated segmentation model for proton density-weighted images. Shoulder MR images from an additional 50 patients (mean age, 33 years; range, 16-65 years; 35 men) were retrospectively collected from May 2014 to April 2019 to create 3D MRI glenohumeral models by transfer learning using Dixon-based sequences. Two musculoskeletal radiologists performed measurements on fully and semiautomated segmented 3D MRI models to assess glenohumeral anatomy, glenoid bone loss (GBL), and their impact on treatment selection. Performance of the CNNs was evaluated using Dice similarity coefficient (DSC), sensitivity, precision, and surface-based distance measurements. Measurements were compared using matched-pairs Wilcoxon signed rank test. Results: The two-dimensional CNN model for the humerus and glenoid achieved a DSC of 0.95 and 0.86, a precision of 95.5% and 87.5%, an average precision of 98.6% and 92.3%, and a sensitivity of 94.8% and 86.1%, respectively. The 3D CNN model, for the humerus and glenoid, achieved a DSC of 0.95 and 0.86, precision of 95.1% and 87.1%, an average precision of 98.7% and 91.9%, and a sensitivity of 94.9% and 85.6%, respectively. There was no difference between glenoid and humeral head width fully and semiautomated 3D model measurements ( P value range, .097-.99). Conclusion: CNNs could potentially be used in clinical practice to provide rapid and accurate 3D MRI glenohumeral bone models and GBL measurements. Supplemental material is available for this article. © RSNA, 2020. (2020 by the Radiological Society of North America, Inc.)
Databáze:	MEDLINE
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