Segmented Spine Research Articles

Automated estimation of thoracic rotation in chest X-ray radiographs: a deep learning approach for enhanced technical assessment.

This study aims to develop an automated approach for estimating the vertical rotation of the thorax, which can be used to assess the technical adequacy of chest X-ray radiographs (CXRs). Total 800 chest radiographs were used to train and establish segmentation networks for outlining the lungs and spine regions in chest X-ray images. By measuring the widths of the left and right lungs between the central line of segmented spine and the lateral sides of the segmented lungs, the quantification of thoracic vertical rotation was achieved. Additionally, a life-size, full body anthropomorphic phantom was employed to collect chest radiographic images under various specified rotation angles for assessing the accuracy of the proposed approach. The deep learning networks effectively segmented the anatomical structures of the lungs and spine. The proposed approach demonstrated a mean estimation error of less than 2° for thoracic rotation, surpassing existing techniques and indicating its superiority. The proposed approach offers a robust assessment of thoracic rotation and presents new possibilities for automated image quality control in chest X-ray examinations. This study presents a novel deep-learning-based approach for the automated estimation of vertical thoracic rotation in chest X-ray radiographs. The proposed method enables a quantitative assessment of the technical adequacy of CXR examinations and opens up new possibilities for automated screening and quality control of radiographs.

ECSU-Net: An Embedded Clustering Sliced U-Net Coupled With Fusing Strategy for Efficient Intervertebral Disc Segmentation and Classification.

Automatic vertebra segmentation from computed tomography (CT) image is the very first and a decisive stage in vertebra analysis for computer-based spinal diagnosis and therapy support system. However, automatic segmentation of vertebra remains challenging due to several reasons, including anatomic complexity of spine, unclear boundaries of the vertebrae associated with spongy and soft bones. Based on 2D U-Net, we have proposed an Embedded Clustering Sliced U-Net (ECSU-Net). ECSU-Net comprises of three modules named segmentation, intervertebral disc extraction (IDE) and fusion. The segmentation module follows an instance embedding clustering approach, where our three sliced sub-nets use axis of CT images to generate a coarse 2D segmentation along with embedding space with the same size of the input slices. Our IDE module is designed to classify vertebra and find the inter-space between two slices of segmented spine. Our fusion module takes the coarse segmentation (2D) and outputs the refined 3D results of vertebra. A novel adaptive discriminative loss (ADL) function is introduced to train the embedding space for clustering. In the fusion strategy, three modules are integrated via a learnable weight control component, which adaptively sets their contribution. We have evaluated classical and deep learning methods on Spineweb dataset-2. ECSU-Net has provided comparable performance to previous neural network based algorithms achieving the best segmentation dice score of 95.60% and classification accuracy of 96.20%, while taking less time and computation resources.

Semi-automated spine and intervertebral disk detection and segmentation from whole spine MR images

Intervertebral disks are found between the vertebrae in the spine from the second cervical vertebra to the sacrum. Intervertebral disks permit various movements of the vertebral column to keep it flexible and to absorb vertical shock. The spine can be exposed to several types of compression forces that can adversely affect disk function and cause diseases or disorders, such as herniated disks. Mainly to diagnose these disorders we use Magnetic Resonance Imaging (MRI), since it's the preferred method for extracting the spinal information needed. In this paper, a Computer Aided Diagnosis (CAD) system for intervertebral disk detection and segmentation, using sagittal MR images of the whole spine has been proposed. The system consists of three major steps as follows: spine region extraction, intervertebral disk detection, and intervertebral disk segmentation. A 6th order polynomial Iterative Randomized Hough Transform (IRHT) approach is used to segment the spine sub-image from a whole spine T1/T2-weighted MR image in a relatively short time. Finally, an elliptical Hough Transform (HT) is used to detect the intervertebral disks from the segmented spine image. The proposed system used a whole spine images dataset consisting of 31 spine T1/T2 MR images. The accuracy measures of intervertebral disk detection and disk segmentation are 94.65% and 90%, respectively. The results show that the proposed system is robust and accurate for spine segmentation and intervertebral disks detection with high performance. Finally, the proposed system is a useful tool for radiologists in detecting as well as segmenting intervertebral disk in whole spine MR images.

Development of a straight vertebrate body axis.

The vertebrate body plan is characterized by the presence of a segmented spine along its main axis. Here, we examine the current understanding of how the axial tissues that are formed during embryonic development give rise to the adult spine and summarize recent advances in the field, largely focused on recent studies in zebrafish, with comparisons to amniotes where appropriate. We discuss recent work illuminating the genetics and biological mechanisms mediating extension and straightening of the body axis during development, and highlight open questions. We specifically focus on the processes of notochord development and cerebrospinal fluid physiology, and how defects in those processes may lead to scoliosis.

Cobb Angle Measurement of Spine from X-Ray Images Using Convolutional Neural Network.

Scoliosis is a common spinal condition where the spine curves to the side and thus deforms the spine. Curvature estimation provides a powerful index to evaluate the deformation severity of scoliosis. In current clinical diagnosis, the standard curvature estimation method for assessing the curvature quantitatively is done by measuring the Cobb angle, which is the angle between two lines, drawn perpendicular to the upper endplate of the uppermost vertebra involved and the lower endplate of the lowest vertebra involved. However, manual measurement of spine curvature requires considerable time and effort, along with associated problems such as interobserver and intraobserver variations. In this article, we propose an automatic system for measuring spine curvature using the anterior-posterior (AP) view spinal X-ray images. Due to the characteristic of AP view images, we first reduced the image size and then used horizontal and vertical intensity projection histograms to define the region of interest of the spine which is then cropped for sequential processing. Next, the boundaries of the spine, the central spinal curve line, and the spine foreground are detected by using intensity and gradient information of the region of interest, and a progressive thresholding approach is then employed to detect the locations of the vertebrae. In order to reduce the influences of inconsistent intensity distribution of vertebrae in the spine AP image, we applied the deep learning convolutional neural network (CNN) approaches which include the U-Net, the Dense U-Net, and Residual U-Net, to segment the vertebrae. Finally, the segmentation results of the vertebrae are reconstructed into a complete segmented spine image, and the spine curvature is calculated based on the Cobb angle criterion. In the experiments, we showed the results for spine segmentation and spine curvature; the results were then compared to manual measurements by specialists. The segmentation results of the Residual U-Net were superior to the other two convolutional neural networks. The one-way ANOVA test also demonstrated that the three measurements including the manual records of two different physicians and our proposed measured record were not significantly different in terms of spine curvature measurement. Looking forward, the proposed system can be applied in clinical diagnosis to assist doctors for a better understanding of scoliosis severity and for clinical treatments.

Seismic Performance and Evaluation of Controlled Spine Frames Applied in High-rise Buildings

A controlled spine frame system consists of moment frames and spine frames with concentrated energy-dissipating members. This system guarantees the continuous usability of buildings against Japanese Level-2 earthquake events (similar to DBE events in California), and the authors have confirmed its excellent performance in preventing damage concentration in low-rise buildings. This study further investigates the effect of diverse structural properties on the seismic performance of controlled spine frames applied in high-rise buildings. The effect of building height, yield drift of dampers, spine-to-moment frame stiffness ratio, and damper-to-moment frame stiffness ratio are illustrated in detail, and optimal values are discussed. Also, a segmented spine frame system is proposed for high-rise buildings. The simple evaluation procedure proposed by the authors for low-rise buildings, based on equivalent linearization techniques and response spectrum analyses, was modified to include higher-mode effects for high-rise buildings based on modal analysis. The modified evaluation method was verified by modal pushover and time-history analyses.

Spine surface detection from local phase-symmetry enhanced ridges in ultrasound images.

Improper administration of epidural anesthesia can result in nerve complications. This problem is exacerbated for obese patients whose vertebrae cannot be palpated. Ultrasound (US) has recently emerged as an attractive imaging modality for accurate epidural placement. However, anesthesiologists untrained in US have difficulty interpreting the anatomy in noisy spinal US images. Furthermore, the complex geometry in spinal US images is characterized by a discontinuous intensity profile because the transducer is often not perpendicularly oriented to spine surface regions such as laminae, articular and transverse processes. This makes the interpretation of spinal images more challenging than typical long bone surface images. In this article, we propose a new method to segment the spine anatomy in US images obtained in both the transverse and paramedian planes. A set of 108 B-mode images were randomly chosen from 35 cine loops obtained from scanning the lumbar and thoracic vertebrae of 17 healthy volunteers with a BMI ranging from 19.5 to 27.9. A local phase-symmetry technique was applied to the B-mode images for enhancement of bone-like ridges, and the spine blobs were subsequently classified. The segmented spine surface from the blobs was compared against a radiologist's manual delineation of the spine surface. For the performance of the spine blob classifier, we obtain a Matthews Correlation Coefficient (MCC) of 0.77 and a geometric mean (G-mean) of 0.96. The mean absolute error between the manual delineation of the laminae by the radiologist and the automatic laminae segmentation is found to be 0.26 mm with a maximum possible absolute error of 2.01 mm for spinal US images of 70 mm depth. Our proposed technique successfully performs automatic segmentation of the spine surface - specifically the laminae, ligamentum flava, spinous, transverse, and articular processes - and can be extended to any bone anatomy present in an US image. This has implications for 3D visualization of bone surfaces and, without loss of generality, the vertebral column.

Bio-inspired control strategy study for the quadruped robot with a segmented spine

PurposeThis study aims to establish a bio-inspired controller for realizing the bounding gait of a quadruped robot system presented in this paper.Design/methodology/approachThe bio-inspired controller is divided into three levels to mimic the biological patterns of animals. First, the high-level sub-controller is equivalent to the cerebellum, which could plan and control the motion of animals. Second, the effect of the middle-level sub-controller corresponds to the central nervous system. The central pattern generators in the spine generate the stable and cyclic signals as the fundamental rhythm for periodic motion of the leg and spine joints. Third, the low-level sub-controller is equal to the end effector, which adopts the simple proportional-derivative (PD) control to realize the specific motion trajectory of the legs and spine.FindingsCombined with the stability criterion presented previously and the delayed feedback control method, the bounding gait of the cheetah virtual prototype could be actuated and stabilized by the bio-inspired controller. Moreover, the bio-inspired controller is applied to realize the bounding gait of an SQBot, which is a quadruped robot with a spine joint. Meanwhile, the validity and practicability of the bio-inspired controller for the control of quadruped robot have been verified against different forward velocities.Originality/valueThe bio-inspired controller and bionic quadruped robot system are instructive for the designing and actuating of the real quadruped robot.

Bionic Control of Cheetah Bounding with a Segmented Spine.

A cheetah model is built to mimic real cheetah and its mechanical and dimensional parameters are derived from the real cheetah. In particular, two joints in spine and four joints in a leg are used to realize the motion of segmented spine and segmented legs which are the key properties of the cheetah bounding. For actuating and stabilizing the bounding gait of cheetah, we present a bioinspired controller based on the state-machine. The controller mainly mimics the function of the cerebellum to plan the locomotion and keep the body balance. The haptic sensor and proprioception system are used to detect the trigger of the phase transition. Besides, the vestibular modulation could perceive the pitching angle of the trunk. At last, the cerebellum acts as the CPU to operate the information from the biological sensors. In addition, the calculated results are transmitted to the low-level controller to actuate and stabilize the cheetah bounding. Moreover, the delay feedback control method is employed to plan the motion of the leg joints to stabilize the pitching motion of trunk with the stability criterion. Finally, the cyclic cheetah bounding with biological properties is realized. Meanwhile, the stability and dynamic properties of the cheetah bounding gait are analyzed elaborately.

Paediatric Neck Modelling using Finite Element Analysis

This study investigated the external and internal biomechanical responses of the segmented paediatric human cervical spine using the finite element modelling approach. The three-dimensional model of the paediatric cervical spine was obtained by modifying our existing finite element model of the adult spine which was derived from computed tomography and cryomicrotomy. The C4-C5-C6 spine simulated the three vertebrae, the anulus fibrosus and the nucleus pulposus of the interconnecting intervertebral discs, and the ligaments. Appropriate anatomical and material property changes were incorporated to include the paediatric components such as the growth plates and the disc nucleus characteristics. The models were exercised under flexion, extension, lateral bending, and axial torsion load vectors. The responses of the paediatric neck model were compared with the adult neck model responses under these loadings. The external response results indicated the paediatric cervical spine to be more flexible than the adult cervical spine under all four modes; in particular, it was most flexible in the axial torsion mode. In addition, the internal stress responses demonstrated increasing magnitudes. While similar increases were found in the external angular flexibilities and the internal disc stresses between the two models, the vertebral body stresses indicated considerably higher relative changes in the paediatric spine, suggesting a higher role for this component under these vectors. These findings highlight the local component mechanics and the variations in the structural behaviours between the adult and paediatric human cervical spines. The present results are a first step in the analysis of the human paediatric cervical spine biomechanical response and will assist in the design of anthropomorphic child dummies for trauma assessment.