Hess Hanspeter, Oswald Alexandra, Rojas J Tomás, Lädermann Alexandre, Zumstein Matthias A, Gerber Kate
Department of Orthopaedic Surgery and Traumatology, Bern University Hospital, Inselspital, University of Bern, Bern, Switzerland.
Shoulder, Elbow and Orthopaedic Sports Medicine, Orthopaedics Sonnenhof, Bern, Switzerland.
Sci Rep. 2025 Jan 10;15(1):1591. doi: 10.1038/s41598-024-84107-7.
Scapular morphological attributes show promise as prognostic indicators of retear following rotator cuff repair. Current evaluation techniques using single-slice magnetic-resonance imaging (MRI) are, however, prone to error, while more accurate computed tomography (CT)-based three-dimensional techniques, are limited by cost and radiation exposure. In this study we propose deep learning-based methods that enable automatic scapular morphological analysis from diagnostic MRI despite the anisotropic resolution and reduced field of view, compared to CT. A deep learning-based segmentation network was trained with paired CT derived scapula segmentations. An algorithm to fuse multi-plane segmentations was developed to generated high-resolution 3D models of the scapula on which morphological landmark- and axes were predicted using a second deep learning network for morphological analysis. Using the proposed methods, the critical shoulder angle, glenoid inclination and version were measured from MRI with accuracies of -1.3 ± 1.7 degrees, 1.3 ± 2.1 degree, and - 1.4 ± 3.4 degrees respectively, compared to CT. Inter-class correlation between MRI and CT derived metrics were substantial for the glenoid version and almost perfect for the other metrics. This study demonstrates how deep learning can overcome reduced resolution, bone border contrast and field of view, to enable 3D scapular morphology analysis on MRI.
肩胛骨形态学特征有望成为肩袖修复术后再撕裂的预后指标。然而，目前使用单层磁共振成像（MRI）的评估技术容易出错，而基于计算机断层扫描（CT）的更精确的三维技术则受到成本和辐射暴露的限制。在本研究中，我们提出了基于深度学习的方法，尽管与CT相比，MRI具有各向异性分辨率和缩小的视野，但仍能从诊断性MRI中实现肩胛骨形态的自动分析。基于深度学习的分割网络使用配对的CT衍生肩胛骨分割进行训练。开发了一种融合多平面分割的算法，以生成肩胛骨的高分辨率三维模型，并使用第二个深度学习网络进行形态分析，预测其上的形态标志和轴。使用所提出的方法，与CT相比，从MRI测量关键肩角、关节盂倾斜度和关节盂扭转的准确率分别为-1.3±1.7度、1.3±2.1度和-1.4±3.4度。MRI和CT衍生指标之间的类间相关性对于关节盂扭转来说较高，而对于其他指标来说几乎是完美的。本研究展示了深度学习如何克服分辨率降低、骨边界对比度和视野问题，从而在MRI上实现三维肩胛骨形态分析。
