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Spine Vision X-Ray Image based GUI Planning of Pedicle Screws Using Enhanced YOLOv5 for Vertebrae Segmentation

arXiv 2407.08349 ↗First posted 11 Jul 2024cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 25 references that are in the map. First posted on arXiv 11 Jul 2024.

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References · 7

  1. 2018YOLOv3: An Incremental Improvementcited by 2,652
  2. 2014Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognitioncited by 719
  3. 2021Coordinate Attention for Efficient Mobile Network Designcited by 239
  4. 2019Learning Spatial Fusion for Single-Shot Object Detectioncited by 59
  5. 2019Automatic Cobb Angle Detection using Vertebra Detector and Vertebra Corners Regressioncited by 8
  6. 2020DC-WCNN: A deep cascade of wavelet based convolutional neural networks for MR Image Reconstructioncited by 8
  7. 2019Liver Segmentation from Multimodal Images using HED-Mask R-CNNcited by 3

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