RESUMEN
BACKGROUND: Automatic segmentation of vertebrae in spinal x-ray images is crucial for clinical diagnosis, case analysis, and surgical planning of spinal lesions. PURPOSE: However, due to the inherent characteristics of x-ray images, including low contrast, high noise, and uneven grey scale, it remains a critical and challenging problem in computer-aided spine image analysis and disease diagnosis applications. METHODS: In this paper, a Multiscale Feature Enhancement Network (MFENet), is proposed for segmenting whole spinal x-ray images, to aid doctors in diagnosing spinal-related diseases. To enhance feature extraction, the network incorporates a Dual-branch Feature Extraction Module (DFEM) and a Semantic Aggregation Module (SAM). The DFEM has a parallel dual-branch structure. The upper branch utilizes multiscale convolutional kernels to extract features from images. Employing convolutional kernels of different sizes helps capture details and structural information at different scales. The lower branch incorporates attention mechanisms to further optimize feature representation. By modeling the feature maps spatially and across channels, the network becomes more focused on key feature regions and suppresses task-irrelevant information. The SAM leverages contextual semantic information to compensate for details lost during pooling and convolution operations. It integrates high-level feature information from different scales to reduce segmentation result discontinuity. In addition, a hybrid loss function is employed to enhance the network's feature extraction capability. RESULTS: In this study, we conducted a multitude of experiments utilizing dataset provided by the Spine Surgery Department of Henan Provincial People's Hospital. The experimental results indicate that our proposed MFENet demonstrates superior segmentation performance in spinal segmentation on x-ray images compared to other advanced methods, achieving 92.61 ± 0.431 for MIoU, 92.42 ± 0.329 for DSC, and 99.51 ± 0.037 for Global_accuracy. CONCLUSIONS: Our model is able to more effectively learn and extract global contextual semantic information, significantly improving spinal segmentation performance, further aiding doctors in analyzing patient conditions.
RESUMEN
In this study, the configuration of a bionic horse robot for equine-assisted therapy is presented. A single-leg system with two degrees of freedom (DOFs) is driven by a cam-linkage mechanism, and it can adjust the span and height of the leg end-point trajectory. After a brief introduction on the quadruped bionic horse robot, the structure and working principle of a single-leg system are discussed in detail. Kinematic analysis of a single-leg system is conducted, and the relationships between the structural parameters and leg trajectory are obtained. On this basis, the pressure angle characteristics of the cam-linkage mechanism are studied, and the leg end-point trajectories of the robot are obtained for several inclination angles controlled by the rotation of the motor for the stride length adjusting. The closed-loop vector method is used for the kinematic analysis, and the motion analysis system is developed in MATLAB software. The motion analysis results are verified by a three-dimensional simulation model developed in Solidworks software. The presented research on the configuration, kinematic modeling, and pressure angle characteristics of the bionic horse robot lays the foundation for subsequent research on the practical application of the proposed bionic horse robot.
RESUMEN
An ankle joint auxiliary rehabilitation robot has been developed, which consists of an upper platform, a lower platform, a dorsiflexion/plantar flexion drive system, a varus/valgus drive system, and some connecting parts. The upper platform connects to the lower platform through a ball pin pair and two driving branch chains based on the S'PS' mechanism. Although the robot has two degrees of freedom (DOF), the upper platform can realize three kinds of motion. To achieve ankle joint auxiliary rehabilitation, the ankle joint of patients on the upper platform makes a bionic motion. The robot uses a centre ball pin pair as the main support to simulate the motion of the ankle joint; the upper platform and the centre ball pin pair construct a mirror image of a patient's foot and ankle joint, which satisfies the human body physiological characteristics; the driving systems adopt a rigid-flexible hybrid structure; and the dorsiflexion/plantar flexion motion and the varus/valgus motion are decoupled. These structural features can avoid secondary damage to the patient. The rehabilitation process is considered, and energy consumption of the robot is studied. An experimental prototype demonstrates that the robot can simulate the motion of the human foot.