Recent years have witnessed wider prevalence of vertebral column pathologies due to lifestyle changes, sedentary behaviors, or injuries. Spondylolisthesis and scoliosis are two of the most common ailments with an incidence of 5% and 3% in the United States population, respectively. Both of these abnormalities can affect children at a young age and, if left untreated, can progress into severe pain. Moreover, severe scoliosis can even lead to lung and heart problems. Thus, early diagnosis can make it easier to apply remedies/interventions and prevent further disease progression. Current diagnosis methods are based on visual inspection by physicians of radiographs and/or calculation of certain angles (e.g., Cobb angle). Traditional artificial intelligence-based diagnosis systems utilized these parameters to perform automated classification, which enabled fast and easy diagnosis supporting tools. However, they still require the specialists to perform error-prone tedious measurements. To this end, automated measurement tools were proposed based on processing techniques of X-ray images. In this paper, we utilize advances in deep transfer learning to diagnose spondylolisthesis and scoliosis from X-ray images without the need for any measurements. We collected raw data from real X-ray images of 338 subjects (i.e., 188 scoliosis, 79 spondylolisthesis, and 71 healthy). Deep transfer learning models were developed to perform three-class classification as well as pair-wise binary classifications among the three classes. The highest mean accuracy and maximum accuracy for three-class classification was 96.73% and 98.02%, respectively. Regarding pair-wise binary classification, high accuracy values were achieved for most of the models (i.e., > 98%). These results and other performance metrics reflect a robust ability to diagnose the subjects’ vertebral column disorders from standard X-ray images. The current study provides a supporting tool that can reasonably help the physicians make the correct early diagnosis with less effort and errors, and reduce the need for surgical interventions.

Using deep transfer learning to detect scoliosis and spondylolisthesis from x-ray images

Scoliosis is a common disease of the spine and requires regular monitoring due to its progressive properties. A preferred indicator to assess scoliosis is by the Cobb angle, which is currently measured either manually by the relevant medical staff or semi-automatically, aided by a computer. These methods are not only labor-intensive but also vary in precision by the inter-observer and intra-observer. Therefore, a reliable and convenient method is urgently needed. With the development of computer vision and deep learning, it is possible to automatically calculate the Cobb angles by processing X-ray or CT/MR/US images. In this paper, the research progress of Cobb angle measurement in recent years is reviewed from the perspectives of computer vision and deep learning. By comparing the measurement effects of typical methods, their advantages and disadvantages are analyzed. Finally, the key issues and their development trends are also discussed.

/pdf/a-review-of-the-methods-on-cobb-angle-measurements-for-1w2lsxkn.pdf

A Review of the Methods on Cobb Angle Measurements for Spinal Curvature

Scoliosis diagnosis and assessment rely upon Cobb angle estimation from X-ray images of the spine. Recently, automated scoliosis assessment has been greatly improved using deep learning methods. However, in such methods, the Cobb angle is usually predicted based on regression models that don't account for information of the spine structure. Alternatively, the Cobb angle can be estimated indirectly through landmark-detection and vertebrasegmentation, but this approach is still highly sensitive to small detection and segmentation errors. This paper proposes a novel deep-learning architecture, called the vertebra localization and tilt estimation network (VLTENet). This network boosts the Cobb angle estimation accuracy through employing vertebra localization and tilt estimation as network prediction goals. In particular, the VLTENet model innovatively combines a deep high-resolution network (HRNet) and a fully-convolutional U-Net architecture for capturing long-range contextual information, the overall structure, and local details in spinal X-ray images. A feature fusion channel attention (FFCA) module is also proposed to selectively emphasize more informative features and suppress less informative ones. In addition, a joint spine loss function (JS-Loss) is designed to account for the spine shape and other spatial constraints, so that the network focuses more on spine-related regions and ignore irrelevant background regions. Finally, we propose a new Cobb angle estimation method conforms with the clinical Cobb angle calculation guidelines, and produces accurate estimates for different types of scoliosis. Extensive experiments on the publically-available AASCE challenge dataset and on an in-house dataset demonstrated the superiority of our method for the task of automatic assessment of scoliosis. 

VLTENet: A Deep-Learning-Based Vertebra Localization and Tilt Estimation Network for Automatic Cobb Angle Estimation

Virtual currency trading develops rapidly, taking up a large proportion in the whole economy, and has become an important part of people’s daily life. Firstly, a reliable virtual currency trading system model is proposed, which is composed of four participant platforms, providers and payers, mainly including the purchase of virtual currency, purchase of virtual goods or services, exchange of virtual currency and other trading activities. In order to monitor possible collusion fraud in virtual currency transactions, payers are introduced to prevent collusion attacks. Video face detection algorithm based on multi-task Cascaded Convolutional Networks is designed and implemented to solve the problems of pose, light and occlusion in the virtual currency trading system. The algorithm utilizes the inherent correlation between detection and calibration to improve the detection performance under the framework of deep cascading tasks. Furthermore, the paper utilizes the three-tiered architecture combined with the well-designed VNN algorithm to realize the face detection and the rough location of key points. Meanwhile, on the basis of in-depth study and analysis of traditional face detection technology, a face detection algorithm based on Harr features and AdaBoost is implemented for comparison and analysis with the aforementioned algorithm. The experimental results show that the method based on MTCNN deep learning network can better extract effective features about faces in videos and achieve more accurate detection. Compared with the traditional method based on AdaBoost and cascade structure, the positive detection rate is much improved.

/pdf/video-face-recognition-of-virtual-currency-trading-system-1w9cucsg7d.pdf

Video Face Recognition of Virtual Currency Trading System Based on Deep Learning Algorithms

“Just Accepted” papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Scoliosis is a disease estimated to affect > 8% of adults in the United States. It is diagnosed via radiography through manual measurement of the angle between maximally tilted vertebrae on a radiograph (Cobb angle). However, these measurements are time-consuming, limiting their use in scoliosis surgical planning and postoperative monitoring. In this retrospective study, the authors developed a pipeline (using the SpineTK architecture) that was trained, validated, and tested on 1310 anterior-posterior EOS images and radiographs from patients with scoliosis (six centers 2005–2020) to automatically measure Cobb angles. The algorithm measured Cobb angles on a hold-out internal test set (n = 460) and external test set (n = 161) with < 2º error (intraclass correlation coefficient: 0.96) compared with ground truth measurements by two experienced radiologists. Measurements, produced in < 0.5 seconds, did not differ significantly (P = .05 cutoff) from ground truth measurements, regardless of presence versus absence of surgical hardware/instrumentation in the image (P = .80), or age (P = .58), sex (P = .83), BMI (P = .63), scoliosis severity (P = .44), or image type (EOS versus radiograph; P = .51) in the patient. These findings suggest that the algorithm is highly robust across different clinical characteristics. Given its automated, rapid, and accurate measurements, this network may be used for monitoring scoliosis progression in patients. ©RSNA, 2023 

Conquering the Cobb Angle: A Deep Learning Algorithm for Automated, Hardware-Invariant Measurement of Cobb Angle on Radiographs in Patients with Scoliosis

The invention discloses a method for automatically measuring a Cobb angle. The method comprises the following steps of step1, preprocessing; step2, carrying out an enhanced watershed segmentation algorithm; step3, extracting each spine center point and fitting a curve; and step4, automatically calculating the Cobb angle. In the method of the invention, upper and lower terminals do not need to be manually established so that the robustness of the algorithm is high, and the method is suitable for operating personnel with less Cobb angle measurement experience; the modes of segmenting-extractingthe center point and fitting the curve are used to represent the curvature of a spine and the method can be further used in the diagnosis of lumbar spondylolisthesis, fracture and other symptoms. Through using the above mode, the curvature of the spine can be effectively and visually displayed. The result of the method is stable and an error is small.

Method for automatically measuring Cobb angle

Cerebral hemorrhage is caused by a number of factors leading to cerebral vascular rupture, which causes blood to flow into the brain tissue and then clumps together to form hematoma. If the treatment is improper for patients with cerebral hemorrhage, their lives will be in great danger. Rapid, accurate and repeatable estimation of cerebral hemorrhage is considerably important for medical diagnosis and treatments. In this paper, an automatic system is proposed to segment the regions of cerebral hemorrhage from CT images. Then the volume of cerebral hemorrhage can be calculated by extracting the regions of cerebral hemorrhage. The segmentation method proposed in this paper is based on the Otsu algorithm, Chicken swarm optimization algorithm and local recursion algorithm. Meanwhile, the results of the proposed algorithm are compared with those given by the experts.

Extraction of Cerebral Hemorrhage and Calculation of Its Volume on CT Image Using Automatic Segmentation Algorithm

Aiming at the subjective experience of the physician, the high measurement error in the Cobb angle measurement of scoliosis X-ray images and the X-ray image of spine is difficult to segment. A deep learning based scoliosis Cobb angle measurement algorithm which can automatically calculate Cobb angle without the physician's manual definition is proposed. A DU-Net detection and segmentation network is proposed in this paper to remove the unrelated regions and to segment the spine contour in the spine X-ray image. The aggregated channel features in pedestrian detection algorithm is introduced to scoliosis image to realize the spine region detection. And the DU-Net network is training to segment spine contour. Therefore, the spine curve can be fitted by the spine contour and the Cobb angle can be automatically measured by the tangent line of spine curve. As a result, the Cobb angle measure methods yields an average error of 2.9° to reference Cobb angle which are measured manually by special orthopaedist. The detection algorithm in this paper yields an average precision of 98.5% and a recall of 99.5%. Moreover, the DU-Net reach an average Dice coefficient to reference segmentation of 90.28%, an IOU of 82.29% and a precision of 86.30%.

https://iopscience.iop.org/article/10.1088/1742-6596/1187/4/042100/pdf

Automatic measurement algorithm of scoliosis Cobb angle based on deep learning

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Method for automatically measuring Cobb angle

Extraction of Cerebral Hemorrhage and Calculation of Its Volume on CT Image Using Automatic Segmentation Algorithm

Automatic measurement algorithm of scoliosis Cobb angle based on deep learning