Segmentation of bone surface from ultrasound using a lightweight network UBS-Net.

Objective. Ultrasound-assisted orthopaedic navigation held promise due to its non-ionizing feature, portability, low cost, and real-time performance. To facilitate the applications, it was critical to have accurate and real-time bone surface segmentation. Nevertheless, the imaging artifacts and low signal-to-noise ratios in the tomographical B-mode ultrasound (B-US) images created substantial challenges in bone surface detection. In this study, we presented an end-to-end lightweight US bone segmentation network (UBS-Net) for bone surface detection.Approach. We presented an end-to-end lightweight UBS-Net for bone surface detection, using the U-Net structure as the base framework and a level set loss function for improved sensitivity to bone surface detectability. A dual attention (DA) mechanism was introduced at the end of the encoder, which considered both position and channel information to obtain the correlation between the position and channel dimensions of the feature map, where axial attention (AA) replaced the traditional self-attention (SA) mechanism in the position attention module for better computational efficiency. The position attention and channel attention (CA) were combined with a two-class fusion module for the DA map. The decoding module finally completed the bone surface detection.Main Results. As a result, a frame rate of 21 frames per second (fps) in detection were achieved. It outperformed the state-of-the-art method with higher segmentation accuracy (Dice similarity coefficient: 88.76% versus 87.22%) when applied the retrospective ultrasound (US) data from 11 volunteers.Significance. The proposed UBS-Net for bone surface detection in ultrasound achieved outstanding accuracy and real-time performance. The new method out-performed the state-of-the-art methods. It had potential in US-guided orthopaedic surgery applications.

Pedicle screw path planning for multi-level vertebral fixation.

BACKGROUND: For 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. PURPOSE: In 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. METHODS: The 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. RESULTS: The 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. CONCLUSIONS: The 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.

Improving pedicle screw path planning by vertebral posture estimation.

Objective.Robot-assisted pedicle screw placement in spinal 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 a high-quality procedure. 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 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 pedicle path planning. In YOLOPOSE3D, the vertebral posture is given as a rotation quaternion and 3D location coordinates by optimizing the intersection over union 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 accurately detect the location and posture of the vertebra 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 a doctor's manual planning, 82.4%. With the clinical class A requirement to allow less than 2 mm 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.