Auteurs:
Hanspeter Hess | School for Biomedical and Precision Engineering, Personalised Medicine Research, University of Bern, Bern, Switzerland | Switzerland
Alexandra Oswald | School for Biomedical and Precision Engineering, Personalised Medicine Research, University of Bern, Bern, Switzerland
Dr. J Tomás Rojas | Shoulder, Elbow and Orthopaedic Sports Medicine, Sonnenhof Orthopaedics, Bern, Switzerland; Department of Orthopaedics and Trauma Surgery, Hospital San José-Clínica Santa María, Santiago, Chile
PD Dr med Alexandre Lädermann | Division of Orthopaedics and Trauma Surgery, La Tour Hospital, Meyrin, Switzerland; Division of Orthopaedics and Trauma Surgery, University of Geneva, Geneva, Switzerland; Faculty of Medicine, University of Geneva, Geneva, Switzerland
Prof. Dr. Matthias A. Zumstein | Shoulder, Elbow and Orthopaedic Sports Medicine, Sonnenhof Orthopaedics, Bern, Switzerland; Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia; Faculty of Medicine, University of Bern, Bern, Switzerland
PD Dr. Kate Gerber | School for Biomedical and Precision Engineering, Personalised Medicine Research, University of Bern, Bern, Switzerland
Introduction – Three-dimensional (3D) scapula morphology measures on CT are more accurate and precise than standard two-dimensional (2D) measurements. Equivalent 3D scapula morphology measurements on diagnostic MRI are impeded, however, due to the reduced image resolution and field of view which excludes the medical and inferior scapular borders. We hypothesised, that deep-learning based algorithms exploiting high resolution knowledge from CT, could enable automatic 3D scapular morphology analysis on diagnostic MRI, with equivalent accuracy to CT.
Methods - A deep-learning based segmentation network for prediction of the scapula from MRI was trained on CT and MRI data from the same shoulder of 20 rotator cuff tear patients. Manual segmentations from CT were aligned to the corresponding MRI and used to train a deep-learning network to automatically segment the scapular on multiplanar MRI (coronal, sagittal and transverse orientations). An algorithm to combine segmentation information from the three planes to generate high-resolution 3D models from the anisotropic MRI data was also developed. For the automatic calculation of common morphological measures, a second deep-learning network was trained to predict the location of anatomical landmarks and scapular axes on the 3D scapula models. Differences between morphology metrics automatically calculated on MRI and on corresponding CT were evaluated on 10 patients using the paired t-test and the intraclass correlation coefficient (ICC).
Results – Morphological measurements were automatically calculated on MRI, with no statistically significant differences from values calculated on CT (P < 0.05). The ICC between values calculated on CT and MRI were: 0.73 for the glenoid version, 0.81 for the glenoid width, 0.89 for the glenoid height, 0.93 for the glenoid inclination and 0.91 for the critical shoulder angle.
Conclusion – This study presents deep learning-based algorithms for automatic scapular morphology analysis from diagnostic MRI. By training deep-learning networks on higher resolution CT based complete scapular models and by combining the information from different MRI planes, challenges posed by the reduced resolution and restricted FOV of diagnostic MR was overcome. Our approach facilitates the application of accurate 3D scapula morphology analysis on patients with diagnostic MRI, eliminating reliance on CT imaging.