An Automatic Path Planning Method of Pedicle Screw Placement Based on Preoperative CT Images

Qi, Xiaozhi; Meng, Jin; Li, Meng; Yang, Yuanyuan; Hu, Ying; Li, Bing; Zhang, Jianwei; Tian, Wei

doi:10.1109/tmrb.2022.3155288

Cited by 13 publications

(9 citation statements)

References 37 publications

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“…Likewise, not only the thread detection but also knot tying and path planning are largely associated with trajectory segmentation [152], [153], [155], [158], [159], [162], [163]. It is worth mentioning that instead of using one single architecture, authors used combination of DL architectures to produce better performance results (e.g.…”

Section: ) Trajectory Segmentationmentioning

confidence: 99%

Deep Learning Based Image Processing for Robot Assisted Surgery: A Systematic Literature Survey

et al. 2022

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The recent advancements in the surging field of Deep Learning (DL) have revolutionized every sphere of life, and the healthcare domain is no exception. The enormous success of DL models, particularly with image data, has led to the development of image-guided Robot Assisted Surgery (RAS) systems. By and large, the number of studies concerning image-driven computer assisted surgical systems using DL has increased exponentially. Additionally, the contemporary availability of surgical datasets has also boosted the DL applications in RAS. Inspired by the latest trends and contributions in surgery, this literature survey presents a summarized analysis of recent innovations of DL in image-guided RAS systems. After a thorough review, a sum of 184 articles are selected and grouped into four categories, based on the literature and the relevancy of the task in the articles, comprising 1) Surgical Tools, 2) Surgical Processes, 3) Surgical Surveillance, and 4) Surgical Performance. The survey also discusses publicly available surgical datasets and highlights the basics of the DL models. Furthermore, the legal, ethical, and technological challenges together with the intuitive predictions and recommendations related to the autonomous RAS systems are also presented. The study reveals that Convolutional Neural Network (CNN) is most widely adopted architecture, whereas, JIGSAWS is most employed dataset in RAS. The study suggests fusing kinematic data along with image data, which produces better accuracy and precision, particularly in gesture and trajectory segmentation tasks. Additionally, CNN and Long Short Term Memory (LSTM) networks have shown remarkable performance, however, authors recommend employing these gigantic architectures only when simpler models have failed to produce satisfactory results. The simpler models, despite their limitations, are time and cost effective and yield considerable outcomes even on the smaller datasets.

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Section: ) Trajectory Segmentationmentioning

confidence: 99%

Deep Learning Based Image Processing for Robot Assisted Surgery: A Systematic Literature Survey

et al. 2022

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“…8 For the computer-aided planning approaches, only single vertebral pedicle screw path planning methods were reported. [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] Wicker et al relies on the geometric constraints of the single vertebral structures, 18 which is further improved by the introduction of safety boundary constraints. 19 More development depends on the correlations between the vertebral bone mineral density (BMD) and the screw placement trajectories from different angles for preoperative screw trajectory planning.…”

Section: Introductionmentioning

confidence: 99%

“…It is a challenging task even for the experienced doctors 8 . For the computer‐aided planning approaches, only single vertebral pedicle screw path planning methods were reported 18‐35 . Wicker et al.…”

Section: Introductionmentioning

confidence: 99%

Pedicle screw path planning for multi‐level vertebral fixation

Zhao,

Zhang,

Zhan

et al. 2024

Medical Physics

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BackgroundFor the spinal internal fixation procedures, connecting rods to the pedicle screws are commonly used in all spinal segments from the cervical to sacral spine. So far, we have only seen single vertebral screw trajectory planning methods in literatures. Joint screw placements in multi‐level vertebrae with the constraint of an ipsilateral connecting rod are not considered.PurposeIn this paper, a screw trajectory planning method that considers screw‐rod joint system with both multi‐level vertebral constraints and individual vertebral safety tolerance are proposed.MethodsThe proposed method addresses three challenging constraints jointly for multi‐level vertebral fixation with pedicle screws. First, a cylindrical screw safe passage model is suggested instead of a unique mathematical optimal trajectory for a single pedicle. Second, the flexible screw cap accessibility model is also included. Third, the connecting rod is modeled to accommodate the spine contour and support the needed gripping capacity. The retrospective clinical data of relative normal shape spines from Beijing Jishuitan hospital were used in the testing. The screw trajectories from the existing methods based on single vertebra and the proposed method based on multi‐level vertebrae optimization are calculated and compared.ResultsThe results showed that the calculated screw placements by the proposed method can achieve 88% success rate without breaking the pedicle cortex and 100% in clinical class A quality (allow less than 2 mm out of the pedicle cortex) compared to 86.1% and 99.1%, respectively, with the existing methods. Expert evaluation showed that the screw path trajectories and the connecting rod calculated by the new method satisfied the clinical implantation requirements.ConclusionsThe new screw planning approach that seeks an overall optimization for multi‐level vertebral fixation is feasible and more advantageous for clinical use than the single vertebral approaches.

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“…Goerres et al (2017) and Vijayan et al (2019) used the atlas method to align the screw path and vertebra on the known vertebra to the target vertebra for screw trajectory planning. Cai et al (2019), Kausch et al (2021), Ma et al (2022) and Qi et al (2022) developed the methods of spine segmentation and preoperative screw trajectory planning based on artificial intelligence (AI).…”

Section: Introductionmentioning

confidence: 99%

Improving pedicle screw path planning by vertebral posture estimation

et al. 2023

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Objective: Robot-assisted pedicle screw placement in spine surgery can reduce the complications associated with the screw placement and reduce the hospital return counts due to malfunctions. However, it requires accurate planning for high quality procedures. The state-of-the-art technologies reported in the literature either ignore the anatomical variations across vertebrae or require substantial human interactions. We present an improved approach that achieves the pedicle screw path planning through multiple projections of a numerically re-oriented vertebra with the estimated posture.Approach: We proposed an improved YOLO-type neural network model (YOLOPOSE3D) to estimate the posture of a vertebra before the pedicle path planning. In YOLOPOSE3D, the vertebral posture is given as a rotation quaternion and 3D location coordinates by optimizing the intersection over union (IoU) of the vertebra with the predicted posture and the actual posture. Then, a new local coordinate system is established for the vertebra based on the estimated posture. Finally, the optimal pedicle screw path trajectory is determined from the multiple projections of the vertebra in the local coordinates. Main results:The experimental results in difficult cases of scoliosis showed that the new YOLOPOSE3D network could detect the location and posture of the vertebra accurately with average translation and orientation errors as small as 1.55 mm and 2.55. The screw path planning achieved 83.1% success rate without breaking the pedicle cortex for the lumbar vertebral L1-L5, which is better than that of doctor’s manual planning, 82.4%. With the clinical class A requirement of allowing less than 2mm out of the pedicle cortex, the success rate achieved nearly 100%. Significance: The proposed YOLOPOSED3D method can accurately determine the vertebral postures. With the improved posture prior, better clinical outcomes can be achieved for pedicle screw placement in spine internal fixation procedures. 

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An Automatic Path Planning Method of Pedicle Screw Placement Based on Preoperative CT Images

Cited by 13 publications

References 37 publications

Deep Learning Based Image Processing for Robot Assisted Surgery: A Systematic Literature Survey

Deep Learning Based Image Processing for Robot Assisted Surgery: A Systematic Literature Survey

Pedicle screw path planning for multi‐level vertebral fixation

Improving pedicle screw path planning by vertebral posture estimation

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