基于脊柱视觉X射线图像、采用增强型YOLOv5进行椎体分割的椎弓根螺钉GUI规划
Spine Vision X-Ray Image based GUI Planning of Pedicle Screws Using Enhanced YOLOv5 for Vertebrae Segmentation
AI总结：
本研究提出基于X射线图像的Spine-Vision GUI系统，结合增强YOLOv5椎体分割技术，支持前后位-侧位同步规划等功能，提升脊柱螺钉置入规划的精准性与安全性。
摘要：
本文提出了一种创新的图形用户界面（Graphical User Interface，GUI），旨在通过椎体分割改进术前规划与术中引导，实现精准的脊柱螺钉置入。该方法涵盖前端与后端计算两部分。前端包含一个GUI，可供外科医生在X射线图像上精准调整螺钉位置，从而优化患者脊柱内螺钉置入的手术模拟效果。另一方面，后端处理包含多个步骤：获取脊柱X射线图像、采用预处理技术降低噪声，以及训练神经网络模型以实现椎体的实时分割。GUI中集成的椎体分割功能可确保螺钉置入精准，减少神经损伤等并发症，最终改善手术效果。Spine-Vision提供了一套全面解决方案，具备前后位-侧位（AP-LP）同步规划、通过椎体分割实现精准螺钉定位、高效螺钉可视化以及动态位置调整等创新特性。这种基于X射线图像的GUI工作流程是一款极具价值的工具，可提升脊柱螺钉置入与规划流程的精准性与安全性。
英文摘要：
In this paper, we propose an innovative Graphical User Interface (GUI) aimed at improving preoperative planning and intra-operative guidance for precise spinal screw placement through vertebrae segmentation. The methodology encompasses both front-end and back-end computations. The front end comprises a GUI that allows surgeons to precisely adjust the placement of screws on X-Ray images, thereby improving the simulation of surgical screw insertion in the patient's spine. On the other hand, the back-end processing involves several steps, including acquiring spinal X-ray images, performing pre-processing techniques to reduce noise, and training a neural network model to achieve real-time segmentation of the vertebrae. The integration of vertebral segmentation in the GUI ensures precise screw placement, reducing complications like nerve injury and ultimately improving surgical outcomes. The Spine-Vision provides a comprehensive solution with innovative features like synchronous AP-LP planning, accurate screw positioning via vertebrae segmentation, effective screw visualization, and dynamic position adjustments. This X-ray image-based GUI workflow emerges as a valuable tool, enhancing precision and safety in spinal screw placement and planning procedures.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
