Objective To develop an automatic diagnostic tool based on deep learning for lumbar spine stability and validate diagnostic accuracy. Methods Preoperative lumbar hyper-flexion and hyper-extension X-ray films were collected from 153 patients with lumbar disease. The following 5 key points were marked by 3 orthopedic surgeons: L4 posteroinferior, anterior inferior angles as well as L5 posterosuperior, anterior superior, and posterior inferior angles. The labeling results of each surgeon were preserved independently, and a total of three sets of labeling results were obtained. A total of 306 lumbar X-ray films were randomly divided into training (n=156), validation (n=50), and test (n=100) sets in a ratio of 3∶1∶2. A new neural network architecture, Swin-PGNet was proposed, which was trained using annotated radiograph images to automatically locate the lumbar vertebral key points and calculate L4, 5 intervertebral Cobb angle and L4 lumbar sliding distance through the predicted key points. The mean error and intra-class correlation coefficient (ICC) were used as an evaluation index, to compare the differences between surgeons’ annotations and Swin-PGNet on the three tasks (key point positioning, Cobb angle measurement, and lumbar sliding distance measurement). Meanwhile, the change of Cobb angle more than 11° was taken as the criterion of lumbar instability, and the lumbar sliding distance more than 3 mm was taken as the criterion of lumbar spondylolisthesis. The accuracy of surgeon annotation and Swin-PGNet in judging lumbar instability was compared. Results ① Key point: The mean error of key point location by Swin-PGNet was (1.407±0.939) mm, and by different surgeons was (3.034±2.612) mm. ② Cobb angle: The mean error of Swin-PGNet was (2.062±1.352)° and the mean error of surgeons was (3.580±2.338)°. There was no significant difference between Swin-PGNet and surgeons (P>0.05), but there was a significant difference between different surgeons (P<0.05). ③ Lumbar sliding distance: The mean error of Swin-PGNet was (1.656±0.878) mm and the mean error of surgeons was (1.884±1.612) mm. There was no significant difference between Swin-PGNet and surgeons and between different surgeons (P>0.05). The accuracy of lumbar instability diagnosed by surgeons and Swin-PGNet was 75.3% and 84.0%, respectively. The accuracy of lumbar spondylolisthesis diagnosed by surgeons and Swin-PGNet was 70.7% and 71.3%, respectively. There was no significant difference between Swin-PGNet and surgeons, as well as between different surgeons (P>0.05). ④ Consistency of lumbar stability diagnosis: The ICC of Cobb angle among different surgeons was 0.913 [95%CI (0.898, 0.934)] (P<0.05), and the ICC of lumbar sliding distance was 0.741 [95%CI (0.729, 0.796)] (P<0.05). The result showed that the annotating of the three surgeons were consistent. The ICC of Cobb angle between Swin-PGNet and surgeons was 0.922 [95%CI (0.891, 0.938)] (P<0.05), and the ICC of lumbar sliding distance was 0.748 [95%CI(0.726, 0.783)] (P<0.05). The result showed that the annotating of Swin-PGNet were consistent with those of surgeons. ConclusionThe automatic diagnostic tool for lumbar instability constructed based on deep learning can realize the automatic identification of lumbar instability and spondylolisthesis accurately and conveniently, which can effectively assist clinical diagnosis.
Medical image registration plays an important role in medical diagnosis and treatment planning. However, the current registration methods based on deep learning still face some challenges, such as insufficient ability to extract global information, large number of network model parameters, slow reasoning speed and so on. Therefore, this paper proposed a new model LCU-Net, which used parallel lightweight convolution to improve the ability of global information extraction. The problem of large number of network parameters and slow inference speed was solved by multi-scale fusion. The experimental results showed that the Dice coefficient of LCU-Net reached 0.823, the Hausdorff distance was 1.258, and the number of network parameters was reduced by about one quarter compared with that before multi-scale fusion. The proposed algorithm shows remarkable advantages in medical image registration tasks, and it not only surpasses the existing comparison algorithms in performance, but also has excellent generalization performance and wide application prospects.
In order to accurately localize the image coordinates and serial numbers of intraoperative subdural matrix electrodes, a matrix electrode localization algorithm for image processing is proposed in this paper. Firstly, by using point-by-point extended electrode location algorithm, the electrode is expanded point-by-point vertically and horizontally, and the initial coordinates and serial numbers of each electrode are determined. Secondly, the single electrode coordinate region extraction algorithm is used to determine the best coordinates of each electrode, so that the image coordinates and serial numbers of all electrodes are determined point-by-point. The results show that the positioning accuracy of electrode serial number is 100%, and the electrode coordinate positioning error is less than 2 mm. The algorithms in this paper can accurately localize the image coordinates and the serial numbers of a matrix electrode arranged in an arc, which could aid drawing of cortical function mapping, and achieve precise positioning of brain functional areas, so that it can be widely used in neuroscience research and clinical application based on electrocorticogram analysis.
The automatic segmentation of auricular acupoint divisions is the basis for realizing intelligent auricular acupoint therapy. However, due to the large number of ear acupuncture areas and the lack of clear boundary, existing solutions face challenges in automatically segmenting auricular acupoints. Therefore, a fast and accurate automatic segmentation approach of auricular acupuncture divisions is needed. A deep learning-based approach for automatic segmentation of auricular acupoint divisions is proposed, which mainly includes three stages: ear contour detection, anatomical part segmentation and keypoints localization, and image post-processing. In the anatomical part segmentation and keypoints localization stages, K-YOLACT was proposed to improve operating efficiency. Experimental results showed that the proposed approach achieved automatic segmentation of 66 acupuncture points in the frontal image of the ear, and the segmentation effect was better than existing solutions. At the same time, the mean average precision (mAP) of the anatomical part segmentation of the K-YOLACT was 83.2%, mAP of keypoints localization was 98.1%, and the running speed was significantly improved. The implementation of this approach provides a reliable solution for the accurate segmentation of auricular point images, and provides strong technical support for the modern development of traditional Chinese medicine.
Objective To improve the fitness and initial fixation strength between the hip and bone and to optimize the shape of the prosthetic implants. Methods The cross-section of hip canal was automatically extracted by Image processing. By using taper curve fit,hypocurve predigesting and the curve of shape center fit, the parameters of long-short diameter and the curve of shape center were got to design the hip shape.CAD was adopted to analyze and evaluate the configuration of bone and shape of hip.The “peg-in-hole” was employed to optimize the shape of implant by the visual test of “Drawingout” in computer. Results 23.2% hip-bone average matching rate and 0.033% bone damage rate were presented by CAD analysis. The implant extraction path were validated visually and quantitatively by measuring the maximum amount of overlap in the path configuration. Conclusion The valuable method for prothsetic hip design was presented by the way of image processing,graphics design and optimizingdesign in this study.
We searched and retrieved literature on the topic of medical image processing published on SCI journals in the past 10 years. We then imported the retrieved literature into TDA for data cleanup before data analysis and processing by EXCLE and UCINET to generate tables and figures that could indicate disciplinary correlation and research hotspots from the perspective of bibliometrics. The results indicated that people in Europe and USA were leading researchers on medical image processing with close international cooperation. Many disciplines contributed to the fast development of medical image processing with intense interdisciplinary researches. The papers that we found show recent research hotspots of the algorithm, system, model, image and segmentation in the field of medical image processing. Cluster analysis on key words of high frequency demonstrated complicated clustering relationship.
Based on repeated experiments as well as continuous researching and improving, an efficient scheme to measure velocity and displacement of the coxa and knee movements based on video image processing technique is presented in this paper. The scheme performed precise and real-time quantitative measurements of 2D velocity or displacement of the coxa and knee using a video camera mounted on one side of the healing and training beds. The beds were based on simplified pinhole projection model. In addition, we used a special-designed auxiliary calibration target, composed by 24 circle points uniformly located on two concentric circles and two straight rods which can rotate freely along the concentric center within the vertical plane, to do the measurements. Experiments carried out in our laboratory showed that the proposed scheme could basically satisfy the requirements about precision and processing speed of such kind of system, and would be very suitable to be applied to smart evaluation/training and healing system for muscles/balance function disability as an advanced and intuitional helping method.
Skin aging principles play important roles in skin disease diagnosis, the evaluation of skin cosmetic effect, forensic identification and age identification in sports competition, etc. This paper proposes a new method to evaluate the skin aging objectively and quantitatively by skin texture area. Firstly, the enlarged skin image was acquired. Then, the skin texture image was segmented by using the iterative threshold method, and the skin ridge image was extracted according to the watershed algorithm. Finally, the skin ridge areas of the skin texture were extracted. The experiment data showed that the average areas of skin ridges, of both men and women, had a good correlation with age (the correlation coefficient r of male was 0.938, and the correlation coefficient r of female was 0.922), and skin texture area and age regression curve showed that the skin texture area increased with age. Therefore, it is effective to evaluate skin aging objectively by the new method presented in this paper.
Objective To lay a foundation for study of optic narve damage in glaucoma by measuring the number and diameter of the optic nerve fibers and optic disc area in normal individuals. Methods The cross-sections of the optic nerve and the optic discs in 15 normal human eyes were examined with the use of a computerized image analysis system. Results The mean nerve fiber count was 10.08times;105plusmn;1.61times;105. The mean nerve fiber diameter was (0.99plusmn;0.04)mu;m. The nerve fiber count increased significantly with the increasing of cross-section area of the optic nerve, but the nerve fiber count was independent of the optic dise area. Conclusion This study provided anatomic basis for predicting the prognosis of optic nerve damage and further studyv of nerve damage in glaucoma. (Chin J Ocul Fundus Dis,1999,15:16-19)