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Found 4 papers, 1 papers with code
Deep learning automates Cobb angle measurement compared with multi-expert observers
To address these challenges and the lack of interpretability found in certain existing automated methods, we have created fully automated software that not only precisely measures the Cobb angle but also provides clear visualizations of these measurements.
SegmentAnyBone: A Universal Model that Segments Any Bone at Any Location on MRI
In our study, we propose a versatile, publicly available deep-learning model for bone segmentation in MRI across multiple standard MRI locations.
Improving Image Classification of Knee Radiographs: An Automated Image Labeling Approach
The final image classification model, trained using both manually labeled and pseudo-labeled data, had the higher weighted average AUC (WAUC: 0. 903) value and higher AUC-ROC values among all classes (normal AUC-ROC: 0. 894; abnormal AUC-ROC: 0. 896, arthroplasty AUC-ROC: 0. 990) compared to the baseline model (WAUC=0. 857; normal AUC-ROC: 0. 842; abnormal AUC-ROC: 0. 848, arthroplasty AUC-ROC: 0. 987), trained using only manually labeled data.
SuperMask: Generating High-resolution object masks from multi-view, unaligned low-resolution MRIs
Three-dimensional segmentation in magnetic resonance images (MRI), which reflects the true shape of the objects, is challenging since high-resolution isotropic MRIs are rare and typical MRIs are anisotropic, with the out-of-plane dimension having a much lower resolution.
