Three-dimensional MRI Bone Models of the Glenohumeral Joint Using Deep Learning: Evaluation of Normal Anatomy and Glenoid Bone Loss
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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. Keywords: Computer Applications-3D, Convolutional Neural Network (CNN), Joints, Long Bones, MR-Imaging, Neural Networks, Segmentation, Shoulder, Skeletal-Appendicular, Supervised learning, Transfer learning © RSNA, 2020
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it