School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, 100876, China.
Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education, Beijing, 100876, China.
Sci Rep. 2024 Jul 13;14(1):16165. doi: 10.1038/s41598-024-65795-7.
When conducting spine-related diagnosis and surgery, the three-dimensional (3D) upright posture of the spine under natural weight bearing is of significant clinical value for physicians to analyze the force on the spine. However, existing medical imaging technologies cannot meet current requirements of medical service. On the one hand, the mainstream 3D volumetric imaging modalities (e.g. CT and MRI) require patients to lie down during the imaging process. On the other hand, the imaging modalities conducted in an upright posture (e.g. radiograph) can only realize 2D projections, which lose the valid information of spinal anatomy and curvature. Developments of deep learning-based 3D reconstruction methods bring potential to overcome the limitations of the existing medical imaging technologies. To deal with the limitations of current medical imaging technologies as is described above, in this paper, we propose a novel deep learning framework, ReVerteR, which can realize automatic 3D Reconstruction of Vertebrae from orthogonal bi-planar Radiographs. With the utilization of self-attention mechanism and specially designed loss function combining Dice, Hausdorff, Focal, and MSE, ReVerteR can alleviate the sample-imbalance problem during the reconstruction process and realize the fusion of the centroid annotation and the focused vertebra. Furthermore, aiming at automatic and customized 3D spinal reconstruction in real-world scenarios, we extend ReVerteR to a clinical deployment-oriented framework, and develop an interactive interface with all functions in the framework integrated so as to enhance human-computer interaction during clinical decision-making. Extensive experiments and visualization conducted on our constructed datasets based on two benchmark datasets of spinal CT, VerSe 2019 and VerSe 2020, demonstrate the effectiveness of our proposed ReVerteR. In this paper, we propose an automatic 3D reconstruction method of vertebrae based on orthogonal bi-planar radiographs. With the 3D upright posture of the spine under natural weight bearing effectively constructed, our proposed method is expected to better support doctors make clinical decision during spine-related diagnosis and surgery.
当进行脊柱相关的诊断和手术时，在自然负重下脊柱的三维（3D）直立姿势对于医生分析脊柱受力具有重要的临床价值。然而，现有的医学成像技术无法满足当前医疗服务的要求。一方面，主流的 3D 容积成像方式（如 CT 和 MRI）要求患者在成像过程中躺下。另一方面，在直立姿势下进行的成像方式（如 X 光片）只能实现 2D 投影，从而丢失了脊柱解剖结构和曲率的有效信息。基于深度学习的 3D 重建方法的发展为克服现有医学成像技术的局限性带来了潜力。为了解决上述当前医学成像技术的局限性，本文提出了一种新颖的深度学习框架 ReVerteR，它可以从正交双平面 X 光片中实现椎体的自动 3D 重建。通过利用自注意力机制和专门设计的损失函数，结合 Dice、Hausdorff、Focal 和 MSE，ReVerteR 可以缓解重建过程中的样本不平衡问题，并实现质心标注和焦点椎体的融合。此外，针对实际场景中自动和定制化的 3D 脊柱重建，我们将 ReVerteR 扩展到一个临床部署导向的框架中，并开发了一个带有所有功能的交互界面，以增强临床决策过程中的人机交互。在基于脊柱 CT 的两个基准数据集 VerSe 2019 和 VerSe 2020 的我们构建的数据集上进行了广泛的实验和可视化，证明了我们提出的 ReVerteR 的有效性。本文提出了一种基于正交双平面 X 光片的椎体自动 3D 重建方法。通过有效构建在自然负重下脊柱的 3D 直立姿势，我们的方法有望更好地支持医生在脊柱相关的诊断和手术中做出临床决策。
