OBJECTIVE: To compare the effectiveness of O-arm navigation and C-arm navigation for guiding percutaneous long sacroiliac screws in treatment of Denis type Ⅱ sacral fractures. METHODS: A retrospective study was conducted on clinical data of the 46 patients with Denis type Ⅱ sacral fractures between April 2021 and October 2022. Among them, 19 patients underwent O-arm navigation assisted percutaneous long sacroiliac screw fixation (O-arm navigation group), and 27 patients underwent C-arm navigation assisted percutaneous long sacroiliac screw fixation (C-arm navigation group). There was no significant difference in gender, age, causes of injuries, Tile classification of pelvic fractures, combined injury, the interval from injury to operation between the two groups ( P>0.05). The intraoperative preparation time, the placement time of each screw, the fluoroscopy time of each screw during placement, screw position accuracy, the quality of fracture reduction, and fracture healing time were recorded and compared, postoperative complications were observed. Pelvic function was evaluated by Majeed score at last follow-up. RESULTS: All operations were completed successfully, and all incisions healed by first intention. Compared to the C-arm navigation group, the O-arm navigation group had shorter intraoperative preparation time, placement time of each screw, and fluoroscopy time, with significant differences ( P<0.05). There was no significant difference in screw position accuracy and the quality of fracture reduction ( P>0.05). There was no nerve or vascular injury during screw placed in the two groups. All patients in both groups were followed up, with the follow-up time of 6-21 months (mean, 12.0 months). Imaging re-examination showed that both groups achieved bony healing, and there was no significant difference in fracture healing time between the two groups ( P>0.05). During follow-up, there was no postoperative complications, such as screw loosening and breaking or loss of fracture reduction. At last follow-up, there was no significant difference in pelvic function between the two groups ( P>0.05). CONCLUSION Compared with the C-arm navigation, the O-arm navigation assisted percutaneous long sacroiliac screws for the treatment of Denis typeⅡsacral fractures can significantly shorten the intraoperative preparation time, screw placement time, and fluoroscopy time, improve the accuracy of screw placement, and obtain clearer navigation images.
Humans ; Fracture Fixation, Internal/methods* ; Retrospective Studies ; Imaging, Three-Dimensional ; Bone Screws ; Surgery, Computer-Assisted ; Tomography, X-Ray Computed ; Spinal Fractures/surgery* ; Fractures, Bone/surgery* ; Pelvic Bones/injuries* ; Postoperative Complications ; Neck Injuries

OBJECTIVES: This study aimed to explore the relationship between alveolar cleft and secondary nasal deformity post unilateral cleft lip repair in adults. METHODS: A total of 27 patients aged 16-30 years old with unilateral secondary nasal deformity and alveolar cleft were included, 13 of whom underwent bone grafting. Spiral CT data of all preoperative and postoperative patients who had alveolar bone grafting were collected. Then, Mimics software was used for three-dimensional reconstruction to evaluate the correlation between the width, height, and volume of the alveolar cleft and those of the nasal deformity. The difference in nasal deformity before and after alveolar bone grafting was also explored. RESULTS: The width of the alveolar cleft was positively correlated with the difference in bilateral nostril floor width (P<0.05). As the effective depth of the alveolar cleft increased, the sub-alare inclination angle largened (P<0.05). However, no significant difference was found in the nasal deformity between before and after alveolar bone grafting. CONCLUSIONS Alveolar cleft is closely related to secondary nasal deformities post unilateral cleft lip repair, especially nasal floor deformities. Alveolar bone grafting benefits adult patients for the improvement of secondary nasal deformities post unilateral cleft lip repair.
Humans ; Adult ; Adolescent ; Young Adult ; Nose/surgery* ; Cleft Lip/surgery* ; Rhinoplasty/methods* ; Imaging, Three-Dimensional ; Treatment Outcome ; Cleft Palate/complications*

Using digital technologies in concurrently performing missing tooth implantation and preparation of remaining teeth is a solution to reduce the number of visits and improve efficiency. This paper proposes a digital process for simultaneously implanting and preparing teeth. It integrates implant surgical guide and 3D-printed tooth preparation guide into a single guide and completes guided implant placement and precise tooth preparation. Based on "repair-oriented" virtual implant planning, the implant surgical guide can improve the efficiency and predictability of implant placement, and its linear accuracy is about 1 mm. The tooth preparation guide precisely guides tooth preparation and restoration space visualization, ensuring the quality of the tooth preparation. The two guides have different design accuracy requirements, and thus their combination improves the overall guiding accuracy requirements. The concurrent application of the two guides minimizes the clinical operation time, number of visits, and economic burden of patients.
Humans ; Surgery, Computer-Assisted ; Dental Implantation, Endosseous ; Printing, Three-Dimensional ; Technology ; Tooth Preparation ; Computer-Aided Design ; Dental Implants ; Imaging, Three-Dimensional ; Cone-Beam Computed Tomography

For patients with partial jaw defects, cysts and dental implants, doctors need to take panoramic X-ray films or manually draw dental arch lines to generate Panorama images in order to observe their complete dentition information during oral diagnosis. In order to solve the problems of additional burden for patients to take panoramic X-ray films and time-consuming issue for doctors to manually segment dental arch lines, this paper proposes an automatic panorama reconstruction method based on cone beam computerized tomography (CBCT). The V-network (VNet) is used to pre-segment the teeth and the background to generate the corresponding binary image, and then the Bezier curve is used to define the best dental arch curve to generate the oral panorama. In addition, this research also addressed the issues of mistakenly recognizing the teeth and jaws as dental arches, incomplete coverage of the dental arch area by the generated dental arch lines, and low robustness, providing intelligent methods for dental diagnosis and improve the work efficiency of doctors.
Humans ; Radiography, Panoramic/methods* ; Cone-Beam Computed Tomography/methods* ; Head ; Image Processing, Computer-Assisted/methods*

Magnetic resonance imaging(MRI) can obtain multi-modal images with different contrast, which provides rich information for clinical diagnosis. However, some contrast images are not scanned or the quality of the acquired images cannot meet the diagnostic requirements due to the difficulty of patient's cooperation or the limitation of scanning conditions. Image synthesis techniques have become a method to compensate for such image deficiencies. In recent years, deep learning has been widely used in the field of MRI synthesis. In this paper, a synthesis network based on multi-modal fusion is proposed, which firstly uses a feature encoder to encode the features of multiple unimodal images separately, and then fuses the features of different modal images through a feature fusion module, and finally generates the target modal image. The similarity measure between the target image and the predicted image in the network is improved by introducing a dynamic weighted combined loss function based on the spatial domain and K-space domain. After experimental validation and quantitative comparison, the multi-modal fusion deep learning network proposed in this paper can effectively synthesize high-quality MRI fluid-attenuated inversion recovery (FLAIR) images. In summary, the method proposed in this paper can reduce MRI scanning time of the patient, as well as solve the clinical problem of missing FLAIR images or image quality that is difficult to meet diagnostic requirements.
Humans ; Deep Learning ; Magnetic Resonance Imaging/methods* ; Image Processing, Computer-Assisted/methods*

Precise segmentation of lung field is a crucial step in chest radiographic computer-aided diagnosis system. With the development of deep learning, fully convolutional network based models for lung field segmentation have achieved great effect but are poor at accurate identification of the boundary and preserving lung field consistency. To solve this problem, this paper proposed a lung segmentation algorithm based on non-local attention and multi-task learning. Firstly, an encoder-decoder convolutional network based on residual connection was used to extract multi-scale context and predict the boundary of lung. Secondly, a non-local attention mechanism to capture the long-range dependencies between pixels in the boundary regions and global context was proposed to enrich feature of inconsistent region. Thirdly, a multi-task learning to predict lung field based on the enriched feature was conducted. Finally, experiments to evaluate this algorithm were performed on JSRT and Montgomery dataset. The maximum improvement of Dice coefficient and accuracy were 1.99% and 2.27%, respectively, comparing with other representative algorithms. Results show that by enhancing the attention of boundary, this algorithm can improve the accuracy and reduce false segmentation.
X-Rays ; Algorithms ; Diagnosis, Computer-Assisted ; Thorax/diagnostic imaging* ; Lung/diagnostic imaging* ; Image Processing, Computer-Assisted

Accurate segmentation of pediatric echocardiograms is a challenging task, because significant heart-size changes with age and faster heart rate lead to more blurred boundaries on cardiac ultrasound images compared with adults. To address these problems, a dual decoder network model combining channel attention and scale attention is proposed in this paper. Firstly, an attention-guided decoder with deep supervision strategy is used to obtain attention maps for the ventricular regions. Then, the generated ventricular attention is fed back to multiple layers of the network through skip connections to adjust the feature weights generated by the encoder and highlight the left and right ventricular areas. Finally, a scale attention module and a channel attention module are utilized to enhance the edge features of the left and right ventricles. The experimental results demonstrate that the proposed method in this paper achieves an average Dice coefficient of 90.63% in acquired bilateral ventricular segmentation dataset, which is better than some conventional and state-of-the-art methods in the field of medical image segmentation. More importantly, the method has a more accurate effect in segmenting the edge of the ventricle. The results of this paper can provide a new solution for pediatric echocardiographic bilateral ventricular segmentation and subsequent auxiliary diagnosis of congenital heart disease.
Adult ; Humans ; Child ; Heart Ventricles/diagnostic imaging* ; Echocardiography ; Image Processing, Computer-Assisted

In recent years, the incidence of thyroid diseases has increased significantly and ultrasound examination is the first choice for the diagnosis of thyroid diseases. At the same time, the level of medical image analysis based on deep learning has been rapidly improved. Ultrasonic image analysis has made a series of milestone breakthroughs, and deep learning algorithms have shown strong performance in the field of medical image segmentation and classification. This article first elaborates on the application of deep learning algorithms in thyroid ultrasound image segmentation, feature extraction, and classification differentiation. Secondly, it summarizes the algorithms for deep learning processing multimodal ultrasound images. Finally, it points out the problems in thyroid ultrasound image diagnosis at the current stage and looks forward to future development directions. This study can promote the application of deep learning in clinical ultrasound image diagnosis of thyroid, and provide reference for doctors to diagnose thyroid disease.
Humans ; Algorithms ; Deep Learning ; Image Processing, Computer-Assisted/methods* ; Thyroid Diseases/diagnostic imaging* ; Ultrasonography

Reconstructing three-dimensional (3D) models from two-dimensional (2D) images is necessary for preoperative planning and the customization of joint prostheses. However, the traditional statistical modeling reconstruction shows a low accuracy due to limited 3D characteristics and information loss. In this study, we proposed a new method to reconstruct the 3D models of femoral images by combining a statistical shape model with Laplacian surface deformation, which greatly improved the accuracy of the reconstruction. In this method, a Laplace operator was introduced to represent the 3D model derived from the statistical shape model. By coordinate transformations in the Laplacian system, novel skeletal features were established and the model was accurately aligned with its 2D image. Finally, 50 femoral models were utilized to verify the effectiveness of this method. The results indicated that the precision of the method was improved by 16.8%-25.9% compared with the traditional statistical shape model reconstruction. Therefore, the method we proposed allows a more accurate 3D bone reconstruction, which facilitates the development of personalized prosthesis design, precise positioning, and quick biomechanical analysis.
Imaging, Three-Dimensional/methods* ; Tomography, X-Ray Computed/methods* ; Femur/surgery* ; Models, Statistical ; Lower Extremity

OBJECTIVE: To compare the effectiveness of O-arm navigation and ultrasound volume navigation (UVN) in guiding screw placement during minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF) surgery. METHODS: Sixty patients who underwent MIS-TLIF surgery for lumbar disc herniation between June 2022 and June 2023 and met the selection criteria were included in the study. They were randomly assigned to group A (screw placement guided by UVN during MIS-TLIF) or group B (screw placement guided by O-arm navigation during MIS-TLIF), with 30 cases in each group. There was no significant difference in baseline data, including gender, age, body mass index, and surgical segment, between the two groups ( P>0.05). Intraoperative data, including average single screw placement time, total radiation dose, and average single screw effective radiation dose, were recorded and calculated. Postoperatively, X-ray film and CT scans were performed at 10 days to evaluate screw placement accuracy and assess facet joint violation. Pearson correlation and Spearman correlation analyses were used to observe the relationship between the studied parameters (average single screw placement time and screw placement accuracy grading) and BMI. RESULTS: The average single screw placement time in group B was significantly shorter than that in group A, and the total radiation dose of single segment and multi-segment and the average single screw effective radiation dose in group B were significantly higher than those in group A ( P<0.05). There was no significant difference in the total radiation dose between single segment and multiple segments in group B ( P>0.05), while the total radiation dose of multiple segments was significantly higher than that of single segment in group A ( P<0.05). No significant difference was found in the accuracy of screw implantation between the two groups ( P>0.05). In both groups, the grade 1 and grade 2 screws broke through the outer wall of the pedicle, and no screw broke through the inner wall of the pedicle. There was no significant difference in the rate of facet joint violation between the two groups ( P>0.05). In group A, both the average single screw placement time and screw placement accuracy grading were positively correlated with BMI ( r=0.677, P<0.001; r=0.222, P=0.012), while in group B, neither of them was correlated with BMI ( r=0.224, P=0.233; r=0.034, P=0.697). CONCLUSION UVN-guided screw placement in MIS-TLIF surgery demonstrates comparable efficiency, visualization, and accuracy to O-arm navigation, while significantly reducing radiation exposure. However, it may be influenced by factors such as obesity, which poses certain limitations.
Humans ; Imaging, Three-Dimensional ; Lumbar Vertebrae/surgery* ; Minimally Invasive Surgical Procedures ; Pedicle Screws ; Retrospective Studies ; Spinal Fusion ; Surgery, Computer-Assisted ; Tomography, X-Ray Computed ; Treatment Outcome