Automated comprehensive Adolescent Idiopathic Scoliosis assessment using MVC-Net

Decentralized convolutional neural network for evaluating spinal deformity with spinopelvic parameters

Low back pain which is caused by the abnormal spinal alignment is one of the most common musculoskeletal symptom and, consequently, is the reason for not only reduction of productivity but also personal suffering. In clinical diagnosis for this disease, estimating adult spinal deformity is required as an indispensable procedure in highlighting abnormal values to output timely warnings and providing precise geometry dimensions for therapeutic therapies. This paper presents an automated method for precisely measuring spinopelvic parameters using a decentralized convolutional neural network as an efficient replacement for current manual process which not only requires experienced surgeons but also shows limitation in ability to process large numbers of images to accommodate the explosion of big data technologies. The proposed method is based on gradually narrowing the regions of interest (ROIs) for feature extraction and leads the model to mainly focus on the necessary geometry characteristics represented as keypoints. According to keypoints obtained, parameters representing the spinal deformity are calculated, which consistency with manual measurement was validated by 40 test cases and, potentially, provided 1.45^o mean absolute values of deviation for PTA as the minimum and 3.51^o in case of LSA as maximum.

Radiographic scoliosis angle estimation: spline-based measurement reveals superior reliability compared to traditional COBB method

INTRODUCTION AND OBJECTIVE

Although being standard for scoliosis curve size estimation, COBB angle measurement is well known to be inaccurate, due to a high interobserver variance in end vertebra selection and end plate contour delineation. We propose a stepwise improvement by using a spline constructed from vertebra centroids to resemble spinal curve characteristics more closely. To enhance precision even further, a neural net was trained to detect the centroids automatically.

MATERIALS & METHODS

Vertebra centroids in AP spinal X-ray images of varying quality from 551 scoliosis patients were manually labeled by 4 investigators. With these inputs, splines were generated and the computed curve sizes were compared to the manually measured COBB angles and to the curve estimation obtained from the neural net.

RESULTS

Splines achieved a higher interobserver correlation of 0.92-0.95 compared to manual COBB measurements (0.83-0.92) and showed 1.5-2 times less variance, depending on the anatomic region. This translates into an average of 1° of interobserver measurement deviation for spline-based curve estimation compared to 3°-8° for COBB measurements. The neural net was even more precise and achieved mean deviations below 0.5°.

CONCLUSION

In conclusion, our data suggest an advantage of spline-based automated measuring systems, so further investigations are warranted to abandon manual COBB measurements.

Automatic Measurement of Spinous Process Angles on Ultrasound Spine Images

Ultrasound (US) imaging technique has been applied to measure the proxy Cobb angle and spinous process angle (SPA) for spinal curvatures of scoliosis. However manual measurement of ultrasound images is time consuming and greatly relying on the experience of raters. The objectives of this work are to develop an automatic measurement method to assess SPA of spine curves and to evaluate the accuracy and reliability of the method. The spinous process curves were identified and fitted on US images, and the automatically measured SPA were compared with the results from US manual and radiographic measurements. It illustrates that the US-auto measurement of SPA presents higher correlation and smaller difference with clinical standard radiographic results than the US-manual measurement.

MB-FSGAN: Joint segmentation and quantification of kidney tumor on CT by the multi-branch feature sharing generative adversarial network

The segmentation of the kidney tumor and the quantification of its tumor indices (i.e., the center point coordinates, diameter, circumference, and cross-sectional area of the tumor) are important steps in tumor therapy. These quantifies the tumor morphometrical details to monitor disease progression and accurately compare decisions regarding the kidney tumor treatment. However, manual segmentation and quantification is a challenging and time-consuming process in practice and exhibit a high degree of variability within and between operators. In this paper, MB-FSGAN (multi-branch feature sharing generative adversarial network) is proposed for simultaneous segmentation and quantification of kidney tumor on CT. MB-FSGAN consists of multi-scale feature extractor (MSFE), locator of the area of interest (LROI), and feature sharing generative adversarial network (FSGAN). MSFE makes strong semantic information on different scale feature maps, which is particularly effective in detecting small tumor targets. The LROI extracts the region of interest of the tumor, greatly reducing the time complexity of the network. FSGAN correctly segments and quantifies kidney tumors through joint learning and adversarial learning, which effectively exploited the commonalities and differences between the two related tasks. Experiments are performed on CT of 113 kidney tumor patients. For segmentation, MB-FSGAN achieves a pixel accuracy of 95.7%. For the quantification of five tumor indices, the R² coefficient of tumor circumference is 0.9465. The results show that the network has reliable performance and shows its effectiveness and potential as a clinical tool.

Toward Automated 3D Spine Reconstruction from Biplanar Radiographs Using CNN for Statistical Spine Model Fitting

To date, 3D spine reconstruction from biplanar radiographs involves intensive user supervision and semi-automated methods that are time-consuming and not effective in clinical routine. This paper proposes a new, fast, and automated 3D spine reconstruction method through which a realistic statistical shape model of the spine is fitted to images using convolutional neural networks (CNN). The CNNs automatically detect the anatomical landmarks controlling the spine model deformation through a hierarchical and gradual iterative process. The performance assessment used a set of 68 biplanar radiographs, composed of both asymptomatic subjects and adolescent idiopathic scoliosis patients, in order to compare automated reconstructions with ground truths build using multiple experts-supervised reconstructions. The mean (SD) errors of landmark locations (3D Euclidean distances) were 1.6 (1.3) mm, 1.8 (1.3) mm, and 2.3 (1.4) mm for the vertebral body center, endplate centers, and pedicle centers, respectively. The clinical parameters extracted from the automated 3D reconstruction (reconstruction time is less than one minute) presented an absolute mean error between 2.8° and 4.7° for the main spinal parameters and between 1° and 2.1° for pelvic parameters. Automated and expert's agreement analysis reported that, on average, 89% of automated measurements were inside the expert's confidence intervals. The proposed automated 3D spine reconstruction method provides an important step that should help the dissemination and adoption of 3D measurements in clinical routine.

Evaluation of a computer-aided method for measuring the Cobb angle on chest X-rays

OBJECTIVES

To automatically measure the Cobb angle and diagnose scoliosis on chest X-rays, a computer-aided method was proposed and the reliability and accuracy were evaluated.

METHODS

Two Mask R-CNN models as the core of a computer-aided method were used to separately detect and segment the spine and all vertebral bodies on chest X-rays, and the Cobb angle of the spinal curve was measured from the output of the Mask R-CNN models. To evaluate the reliability and accuracy of the computer-aided method, the Cobb angles on 248 chest X-rays from lung cancer screening were measured automatically using a computer-aided method, and two experienced radiologists used a manual method to separately measure Cobb angles on the aforementioned chest X-rays.

RESULTS

For manual measurement of the Cobb angle on chest X-rays, the intraclass correlation coefficients (ICC) of intra- and inter-observer reliability analysis was 0.941 and 0.887, respectively, and the mean absolute differences were < 3.5°. The ICC between the computer-aided and manual methods for Cobb angle measurement was 0.854, and the mean absolute difference was 3.32°. These results indicated that the computer-aided method had good reliability for Cobb angle measurement on chest X-rays. Using the mean value of Cobb angles in manual measurements > 10° as a reference standard for scoliosis, the computer-aided method achieved a high level of sensitivity (89.59%) and a relatively low level of specificity (70.37%) for diagnosing scoliosis on chest X-rays.

CONCLUSION

The computer-aided method has potential for automatic Cobb angle measurement and scoliosis diagnosis on chest X-rays. These slides can be retrieved under Electronic Supplementary Material.

Fully automated radiological analysis of spinal disorders and deformities: a deep learning approach

PURPOSE

We present an automated method for extracting anatomical parameters from biplanar radiographs of the spine, which is able to deal with a wide scenario of conditions, including sagittal and coronal deformities, degenerative phenomena as well as images acquired with different fields of view.

METHODS

The location of 78 landmarks (end plate centers, hip joint centers, and margins of the S1 end plate) was extracted from three-dimensional reconstructions of 493 spines of patients suffering from various disorders, including adolescent idiopathic scoliosis, adult deformities, and spinal stenosis. A fully convolutional neural network featuring an additional differentiable spatial to numerical (DSNT) layer was trained to predict the location of each landmark. The values of some parameters (T4-T12 kyphosis, L1-L5 lordosis, Cobb angle of scoliosis, pelvic incidence, sacral slope, and pelvic tilt) were then calculated based on the landmarks' locations. A quantitative comparison between the predicted parameters and the ground truth was performed on a set of 50 patients.

RESULTS

The spine shape predicted by the models was perceptually convincing in all cases. All predicted parameters were strongly correlated with the ground truth. However, the standard errors of the estimated parameters ranged from 2.7° (for the pelvic tilt) to 11.5° (for the L1-L5 lordosis).

CONCLUSIONS

The proposed method is able to automatically determine the spine shape in biplanar radiographs and calculate anatomical and posture parameters in a wide scenario of clinical conditions with a very good visual performance, despite limitations highlighted by the statistical analysis of the results. These slides can be retrieved under Electronic Supplementary Material.

Artificial intelligence and machine learning in spine research

Artificial intelligence (AI) and machine learning (ML) techniques are revolutionizing several industrial and research fields like computer vision, autonomous driving, natural language processing, and speech recognition. These novel tools are already having a major impact in radiology, diagnostics, and many other fields in which the availability of automated solution may benefit the accuracy and repeatability of the execution of critical tasks. In this narrative review, we first present a brief description of the various techniques that are being developed nowadays, with special focus on those used in spine research. Then, we describe the applications of AI and ML to problems related to the spine which have been published so far, including the localization of vertebrae and discs in radiological images, image segmentation, computer-aided diagnosis, prediction of clinical outcomes and complications, decision support systems, content-based image retrieval, biomechanics, and motion analysis. Finally, we briefly discuss major ethical issues related to the use of AI in healthcare, namely, accountability, risk of biased decisions as well as data privacy and security, which are nowadays being debated in the scientific community and by regulatory agencies.