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Open source software for automatic subregional assessment of knee cartilage degradation using quantitative T2 relaxometry and deep learning., , , , , , , , , and . CoRR, (2020)Detecting Anatomical Landmarks for Motion Estimation in Weight-Bearing Imaging of Knees., , , , , , , and . MLMIR@MICCAI, volume 11074 of Lecture Notes in Computer Science, page 83-90. Springer, (2018)Multi-Channel Volumetric Neural Network for Knee Cartilage Segmentation in Cone-Beam CT., , , , , , , , , and . Bildverarbeitung für die Medizin, page 67-72. Springer, (2020)Rigid and Non-Rigid Motion Compensation in Weight-Bearing CBCT of the Knee Using Simulated Inertial Measurements., , , , , , and . IEEE Trans. Biomed. Eng., 69 (5): 1608-1619 (2022)Motion Compensation Using Range Imaging in C-Arm Cone-Beam CT., , , , , , , and . MIUA, volume 723 of Communications in Computer and Information Science, page 561-570. Springer, (2017)Deep Learning Super-Resolution Enables Rapid Simultaneous Morphological and Quantitative Magnetic Resonance Imaging., , , , and . MLMIR@MICCAI, volume 11074 of Lecture Notes in Computer Science, page 3-11. Springer, (2018)Epipolar Consistency Conditions for Motion Correction in Weight-Bearing Imaging., , , , , , , and . Bildverarbeitung für die Medizin, page 209-214. Springer, (2017)Fourier-based Reduction of Directed Streak Artifacts in Cone-Beam CT., , , and . Bildverarbeitung für die Medizin, page 127-132. Springer Vieweg, (2018)Multi-Channel Volumetric Neural Network for Knee Cartilage Segmentation in Cone-beam CT., , , , , , , , , and . CoRR, (2019)Comparison of Different Approaches for Measuring Tibial Cartilage Thickness., , , , , , , , , and . J. Integr. Bioinform., (2017)