Ultrasound Volume Projection Imaging for Assessment of Scoliosis

SSAT-Swin: Deep Learning-Based Spinal Ultrasound Feature Segmentation for Scoliosis Using Self-Supervised Swin Transformer

OBJECTIVE

Scoliosis, a 3-D spinal deformity, requires early detection and intervention. Ultrasound curve angle (UCA) measurement using ultrasound images has emerged as a promising diagnostic tool. However, calculating the UCA directly from ultrasound images remains challenging due to low contrast, high noise, and irregular target shapes. Accurate segmentation results are therefore crucial to enhance image clarity and precision prior to UCA calculation.

METHODS

We propose the SSAT-Swin model, a transformer-based multi-class segmentation framework designed for ultrasound image analysis in scoliosis diagnosis. The model integrates a boundary-enhancement module in the decoder and a channel attention module in the skip connections. Additionally, self-supervised proxy tasks are used during pre-training on 1,170 images, followed by fine-tuning on 109 image-label pairs.

RESULTS

The SSAT-Swin achieved Dice scores of 85.6% and Jaccard scores of 74.5%, with a 92.8% scoliosis bone feature detection rate, outperforming state-of-the-art models.

CONCLUSION

Self-supervised learning enhances the model's ability to capture global context information, making it well-suited for addressing the unique challenges of ultrasound images, ultimately advancing scoliosis assessment through more accurate segmentation.

Anatomy-inspired model for critical landmark localization in 3D spinal ultrasound volume data

Three-dimensional (3D) spinal ultrasound imaging has demonstrated its promising potential in measuring spinal deformity through recent studies, and it is more suitable for massive early screening and longitudinal follow-up of adolescent idiopathic scoliosis (AIS) compared with X-ray imaging due to its radiation-free superiority. Moreover, some deformities with low observability, such as vertebral rotation, in X-ray images can also be reflected by critical landmarks in 3D ultrasound data. In this paper, we propose a localization network (LLNet) to extract lamina in 3D ultrasound data, which has been indicated as a meaningful anatomy for measuring vertebral rotation by clinical studies. First, the LLNet skillfully establishes a parallel anatomical prior embedding branch that implicitly explores the anatomical correlation between the lamina and another anatomy with more stable observability (spinous process) during the training phase and then introduces the correlation to highlight the potential region of the lamina in the inferring one. Second, since the lamina is a tiny target, the information loss caused by continuous convolutional and pooling operations has a profound negative effect on detecting the lamina. We employ an optimization mechanism to mitigate this problem, which refines feature maps according to information from the original image and further reuses them to polish output. Furthermore, a modified global-local attention module is deployed on skip connections to mine global dependencies and contextual information to construct an effective image pattern. Extensive comparisons and ablation studies are performed on actual clinical data. Results indicate that the capability of our model is better than other outstanding detection models, and functional modules effectively contribute to this, with a 100.0 % detection success rate and an 8.9 % improvement of mean intersection over the union. Thus, our model is promising to become a helpful part of a computer-assisted diagnosis system based on 3D spinal ultrasound imaging.

Ultrasound vs. x-ray: a new way for clinicians to track scoliosis progression?

This retrospective study, utilising prospectively collected data, investigates the use of spine ultrasound as an alternative method for assessing scoliosis, with the aim of reducing radiation exposure. We included 92 patients aged 10 to 16 years with suspected idiopathic scoliosis. Exclusion criteria were weight over 150 kg, metal implants, pre-existing conditions, secondary deformities, and cognitive impairments. Each patient underwent clinical assessment and full spine radiographs, followed by spine ultrasound using the Scolioscan® system. Unprocessed B-mode ultrasound images were analysed using automatic measurements. The correlation between Ultrasound Coronal Angle (UCA) and Radiographic Cobb Angle (RCA) was evaluated at initial and follow-up visits. Strong correlations were found between UCA and RCA, with correlation coefficients ranging from 0.786 to 0.903 (p<0.001). The regression formula showed good predictive accuracy for curve progression on follow-up radiographs. The best results were observed in females and in primary thoracic curves (r = 0.936, p<0.001). Although only four patients exhibited true progression (≥5° increase in Cobb angle), changes in scoliotic angles were effectively detected using ultrasound. This study confirms the feasibility of unprocessed spine ultrasound for scoliosis monitoring in clinical settings. Automatic measurements without 3D reconstruction make ultrasound a practical tool for tracking progression. The regression model shows potential for predicting curve progression, although further validation is needed. These findings suggest spine ultrasound could reduce the need for radiographs, benefiting patients by minimising radiation exposure while providing reliable monitoring of scoliosis progression and treatment outcomes.

Three-dimensional (3D) ultrasound imaging for quantitative assessment of frontal cobb angles in patients with idiopathic scoliosis - a systematic review and meta-analysis

BACKGROUND

Measurement of Cobb angle in the frontal plane from radiographs is the gold standard of quantifying spinal deformity in adolescent idiopathic scoliosis (AIS). As a radiation free alternative, ultrasonography (USG) for quantitative measurement of frontal cobb angles has been reported. However, a systematic review and meta-analysis on the reliability of ultrasound comparing with the gold standard have not yet been reported.

OBJECTIVES

This systematic review and meta-analysis aimed to evaluate (1) the reliability of ultrasound imaging compared with radiographs in measuring frontal cobb angle for screening or monitoring in AIS patients; (2) whether the performance of USG differ when using different anatomical landmarks for measurement of frontal cobb angles.

METHODS

Systematic search was performed on MEDLINE, EMBASE, CINAHL, and CENTRAL databases for relevant studies. QUADAS-2 was adopted for quality assessment. The intra- and inter-rater reliability of ultrasound measurement in terms of intra-class correlation coefficient (ICC) was recorded. Mean Absolute Difference (MAD) and Pearson correlation coefficients between frontal cobb angle measured from USG and radiographic measurements, were extracted with meta-analysis performed.

RESULTS AND DISCUSSION

Nineteen studies were included with a total of 2318 patients. The risk of bias of included studies were unclear or high. Pooled MAD of frontal cobb angle measured between USG and radiography was 4.02 degrees (95% CI: 3.28-4.76) with a pooled correlation coefficient of 0.91 (95% CI: 0.87-0.93). Subgroup analyses show that pooled correlation was > 0.87 across using various USG landmarks for measurement of frontal cobb angles. There was a high level of heterogeneity between results of the included studies with I2 > 90%. Potential sources of heterogeneity include curve severity, curve types, location of apex, scanning postures, patient demographics, equipment, and operator experience. Despite being the "gold standard", intrinsic errors in quantifying spinal deformities with radiographs may also be a source of inconsistency.

CONCLUSION

The current systematic review indicated that there is evidence in favor of using USG for quantitative evaluation of frontal cobb angle in AIS. However, the quality of evidence is low due to high risk of bias and heterogeneity between existing studies. Current literature is insufficient to support the use of USG as a screening and/or follow-up method for AIS. Further investigation addressing the limitations identified in this review is required before USG could be adapted for further clinical use.

Real-Time Volumetric Free-Hand Ultrasound Imaging for Large-Sized Organs: A Study of Imaging the Whole Spine

OBJECTIVES

Three-dimensional (3D) ultrasound imaging can overcome the limitations of conventional two-dimensional (2D) ultrasound imaging in structural observation and measurement. However, conducting volumetric ultrasound imaging for large-sized organs still faces difficulties including long acquisition time, inevitable patient movement, and 3D feature recognition. In this study, we proposed a real-time volumetric free-hand ultrasound imaging system optimized for the above issues and applied it to the clinical diagnosis of scoliosis.

METHODS

This study employed an incremental imaging method coupled with algorithmic acceleration to enable real-time processing and visualization of the large amounts of data generated when scanning large-sized organs. Furthermore, to deal with the difficulty of image feature recognition, we proposed two tissue segmentation algorithms to reconstruct and visualize the spinal anatomy in 3D space by approximating the depth at which the bone structures are located and segmenting the ultrasound images at different depths.

RESULTS

We validated the adaptability of our system by deploying it to multiple models of ultrasound equipment and conducting experiments using different types of ultrasound probes. We also conducted experiments on six scoliosis patients and 10 normal volunteers to evaluate the performance of our proposed method. Ultrasound imaging of a volunteer spine from shoulder to crotch (more than 500 mm) was performed in 2 minutes, and the 3D imaging results displayed in real-time were compared with the corresponding X-ray images with a correlation coefficient of 0.96 in spinal curvature.

CONCLUSION

Our proposed volumetric ultrasound imaging system might hold the potential to be clinically applied to other large-sized organs.

Automatic 3-D Lamina Curve Extraction From Freehand 3-D Ultrasound Data Using Sequential Localization Recurrent Convolutional Networks

Freehand 3-D ultrasound imaging is emerging as a promising modality for regular spine exams due to its noninvasiveness and affordability. The laminae landmarks play a critical role in depicting the 3-D shape of the spine. However, the extraction of the 3-D lamina curves from transverse ultrasound sequences presents a challenging task, primarily attributed to the presence of diverse contrast variations, imaging artifacts, the complex surface of vertebral bones, and the difficulties associated with probe manipulation. This article proposes sequential localization recurrent convolutional networks (SL-RCNs), a novel deep learning model that takes the contextual relationships into account and embeds the transformation matrix feature as a 3-D knowledge base to enhance accurate ultrasound sequence analysis. The assessment involved the analysis of 3-D ultrasound sequences obtained from ten healthy adult human participants, covering both the lumbar and thoracic regions. The performance of SL-RCN is evaluated through sevenfold cross-validation, using the leave-one-participant-out strategy. The validity of AI model training is assessed on test data from three participants. Normalized discrete Fréchet distance (NDFD) is used as the main metric to evaluate the disparity of the extracted 3-D lamina curves. In contrast to our previous 2-D image analysis method, SL-RCN generates reduced left/right mean distance errors (MDEs) from 1.62/1.63 to 1.41/1.40 mm, and NDFDs from 0.5910/0.6389 to 0.4276/0.4567. The increase in the mean NDFD value from sevenfold cross-validation to the test data experiment is less than 0.05. The experiments demonstrate the SL-RCN's capability in extracting accurate paired smooth lamina landmark curves.

Next-Gen Medical Imaging: U-Net Evolution and the Rise of Transformers

The advancement of medical imaging has profoundly impacted our understanding of the human body and various diseases. It has led to the continuous refinement of related technologies over many years. Despite these advancements, several challenges persist in the development of medical imaging, including data shortages characterized by low contrast, high noise levels, and limited image resolution. The U-Net architecture has significantly evolved to address these challenges, becoming a staple in medical imaging due to its effective performance and numerous updated versions. However, the emergence of Transformer-based models marks a new era in deep learning for medical imaging. These models and their variants promise substantial progress, necessitating a comparative analysis to comprehend recent advancements. This review begins by exploring the fundamental U-Net architecture and its variants, then examines the limitations encountered during its evolution. It then introduces the Transformer-based self-attention mechanism and investigates how modern models incorporate positional information. The review emphasizes the revolutionary potential of Transformer-based techniques, discusses their limitations, and outlines potential avenues for future research.

Automatic Assessment of Ultrasound Curvature Angle for Scoliosis Detection Using 3-D Ultrasound Volume Projection Imaging

OBJECTIVE

Scoliosis is a spinal deformation in which the spine takes a lateral curvature, generating an angle in the coronal plane. The conventional method for detecting scoliosis is measurement of the Cobb angle in spine images obtained by anterior X-ray scanning. Ultrasound imaging of the spine is found to be less ionising than traditional radiographic modalities. For posterior ultrasound scanning, alternate indices of the spinous process angle (SPA) and ultrasound curve angle (UCA) were developed and have proven comparable to those of the traditional Cobb angle. In SPA, the measurements are made using the spinous processes as an anatomical reference, leading to an underestimation of the traditionally used Cobb angles. Alternatively, in UCA, more lateral features of the spine are employed for measurement of the main thoracic and thoracolumbar angles; however, clear identification of bony features is required. The current practice of UCA angle measurement is manual. This research attempts to automate the process so that the errors related to human intervention can be avoided and the scalability of ultrasound scoliosis diagnosis can be improved. The key objective is to develop an automatic scoliosis diagnosis system using 3-D ultrasound imaging.

METHODS

The novel diagnosis system is a three-step process: (i) finding the ultrasound spine image with the most visible lateral features using the convolutional RankNet algorithm; (ii) segmenting the bony features from the noisy ultrasound images using joint spine segmentation and noise removal; and (iii) calculating the UCA automatically using a newly developed centroid pairing and inscribed rectangle slope method.

RESULTS

The proposed method was evaluated on 109 patients with scoliosis of different severity. The results obtained had a good correlation with manually measured UCAs (R2=0.9784 for the main thoracic angle andR2=0.9671 for the thoracolumbar angle) and a clinically acceptable mean absolute difference of the main thoracic angle (2.82 ± 2.67°) and thoracolumbar angle (3.34 ± 2.83°).

CONCLUSION

The proposed method establishes a very promising approach for enabling the applications of economic 3-D ultrasound volume projection imaging for mass screening of scoliosis.

Anatomical Prior and Inter-Slice Consistency for Semi-Supervised Vertebral Structure Detection in 3D Ultrasound Volume

Three-dimensional (3D) ultrasound imaging technique has been applied for scoliosis assessment, but the current assessment method only uses coronal projection images and cannot illustrate the 3D deformity and vertebra rotation. The vertebra detection is essential to reveal 3D spine information, but the detection task is challenging due to complex data and limited annotations. We propose VertMatch to detect vertebral structures in 3D ultrasound volume containing a detector and classifier. The detector network finds the potential positions of structures on transverse slice globally, and then the local patches are cropped based on detected positions. The classifier is used to distinguish whether the patches contain real vertebral structures and screen the predicted positions from the detector. VertMatch utilizes unlabeled data in a semi-supervised manner, and we develop two novel techniques for semi-supervised learning: 1) anatomical prior is used to acquire high-quality pseudo labels; 2) inter-slice consistency is used to utilize more unlabeled data by inputting multiple adjacent slices. Experimental results demonstrate that VertMatch can detect vertebra accurately in ultrasound volume and outperforms state-of-the-art methods. Moreover, VertMatch is also validated in automatic spinous process angle measurement on forty subjects with scoliosis, and the results illustrate that it can be a promising approach for the 3D assessment of scoliosis.

Development of Automatic Assessment Framework for Spine Deformity Using Freehand 3-D Ultrasound Imaging System

A 3-D ultrasound (US) imaging technique has been studied to facilitate the diagnosis of spinal deformity without radiation. The objective of this article is to propose an assessment framework to automatically estimate spinal deformity in US spine images. The proposed framework comprises four major components, a US spine image generator, a novel transformer-based lightweight spine detector network, an angle evaluator, and a 3-D modeler. The principal component analysis (PCA) and discriminative scale space tracking (DSST) method are first adopted to generate the US spine images. The proposed detector is equipped with a redundancy queries removal (RQR) module and a regularization item to realize accurate and unique detection of spine images. Two clinical datasets, a total of 273 images from adolescents with idiopathic scoliosis, are used for the investigation of the proposed framework. The curvature is estimated by the angle evaluator, and the 3-D mesh model is established by the parametric modeling technique. The accuracy rate (AR) of the proposed detector can be achieved at 99.5%, with a minimal redundancy rate (RR) of 1.5%. The correlations between automatic curve measurements on US spine images from two datasets and manual measurements on radiographs are 0.91 and 0.88, respectively. The mean absolute difference (MAD) and standard deviation (SD) are 2.72° ± 2.14° and 2.91° ± 2.36° , respectively. The results demonstrate the effectiveness of the proposed framework to advance the application of the 3-D US imaging technique in clinical practice for scoliosis mass screening and monitoring.

Monitoring of Curve Progression in Patients with Adolescent Idiopathic Scoliosis Using 3-D Ultrasound

OBJECTIVE

The aim of the work described here was to determine whether 3-D ultrasound can provide results comparable to those of conventional X-ray examination in assessing curve progression in patients with adolescent idiopathic scoliosis (AIS).

METHODS

One hundred thirty-six participants with AIS (42 males and 94 females; age range: 10-18 y, mean age: 14.1 ± 1.9 y) with scoliosis of different severity (Cobb angle range: 10º- 85º, mean: of 24.3 ± 14.4º) were included. Each participant underwent biplanar low-dose X-ray EOS and 3-D ultrasound system scanning with the same posture on the same date. Participants underwent the second assessment at routine clinical follow-up. Manual measurements of scoliotic curvature on ultrasound coronal projection images and posterior-anterior radiographs were expressed as the ultrasound curve angle (UCA) and radiographic Cobb angle (RCA), respectively. RCA and UCA increments ≥5º represented a scoliosis progression detected by X-ray assessment and 3-D ultrasound assessment, respectively.

RESULTS

The sensitivity and specificity of UCA measurement in detecting scoliosis progression were 0.93 and 0.90, respectively. The negative likelihood ratio of the diagnostic test for scoliosis progression by the 3-D ultrasound imaging system was 0.08.

CONCLUSION

The 3-D ultrasound imaging method is a valid technique for detecting coronal curve progression as compared with conventional radiography in follow-up of AIS. Substituting conventional radiography with 3-D ultrasound is effective in reducing the radiation dose to which AIS patients are exposed during their follow-up examinations.

A feasibility study of application of purpose-design frame and 3-D clinical ultrasound in assessment and design of spinal orthoses for adolescent idiopathic scoliosis

BACKGROUND

The immediate in-orthosis correction of adolescent idiopathic scoliosis (AIS) is a useful prognostic parameter for the long-term orthotic treatment outcome. The 3-D clinical ultrasound technique is considered a noninvasive alternative to assess scoliotic deformities that could be applied in the orthotic treatment of AIS.

OBJECTIVE

This study aimed to investigate the feasibility of a purpose-design assessment frame in estimating biomechanical effects of the controlling pads of a spinal orthosis under the guidance of the ultrasound system.

METHODS

Twenty-six subjects with AIS were recruited and arranged to position inside the assessment frame, and controlling pads were applied strategically while the scoliotic deformities were assessed by clinical ultrasound to obtain at least 30% curvature correction, and the body shape was then captured using a computer-aided design and computer-aided manufacture system, and spinal orthoses were subsequently fabricated. The preorthosis and immediate in-orthosis coronal and sagittal X-rays were used for comparison.

RESULTS

X-ray assessments showed that the mean coronal Cobb angle and lumbar lordosis of the subjects from the preorthosis to immediate in-orthosis visits decreased significantly ( p < 0.05) from 29.6° to 16.6°, and from 47.2° to 35.3°, respectively.

CONCLUSIONS

This feasibility study showed that the proposed method would have a good potential to improve orthotic treatment outcome in a documented approach that should be considered for implementation into routine clinical practice aiming to reduce the chance of deformity deterioration leading to surgical intervention. However, a controlled group study is required to compare the results.

Si-MSPDNet: A multiscale Siamese network with parallel partial decoders for the 3-D measurement of spines in 3D ultrasonic images

Early screening and frequent monitoring effectively decrease the risk of severe scoliosis, but radiation exposure is a consequence of traditional radiograph examinations. Additionally, traditional X-ray images on the coronal or sagittal plane have difficulty providing three-dimensional (3-D) information on spinal deformities. The Scolioscan system provides an innovative 3-D spine imaging approach via ultrasonic scanning, and its feasibility has been demonstrated in numerous studies. In this paper, to further examine the potential of spinal ultrasonic data for describing 3-D spinal deformities, we propose a novel deep-learning tracker named Si-MSPDNet for extracting widely employed landmarks (spinous process (SP)) in ultrasonic images of spines and establish a 3-D spinal profile to measure 3-D spinal deformities. Si-MSPDNet has a Siamese architecture. First, we employ two efficient two-stage encoders to extract features from the uncropped ultrasonic image and the patch centered on the SP cut from the image. Then, a fusion block is designed to strengthen the communication between encoded features and further refine them from channel and spatial perspectives. The SP is a very small target in ultrasonic images, so its representation is weak in the highest-level feature maps. To overcome this, we ignore the highest-level feature maps and introduce parallel partial decoders to localize the SP. The correlation evaluation in the traditional Siamese network is also expanded to multiple scales to enhance cooperation. Furthermore, we propose a binary guided mask based on vertebral anatomical prior knowledge, which can further improve the performance of our tracker by highlighting the potential region with SP. The binary-guided mask is also utilized for fully automatic initialization in tracking. We collected spinal ultrasonic data and corresponding radiographs on the coronal and sagittal planes from 150 patients to evaluate the tracking precision of Si-MSPDNet and the performance of the generated 3-D spinal profile. Experimental results revealed that our tracker achieved a tracking success rate of 100% and a mean IoU of 0.882, outperforming some commonly used tracking and real-time detection models. Furthermore, a high correlation existed on both the coronal and sagittal planes between our projected spinal curve and that extracted from the spinal annotation in X-ray images. The correlation between the tracking results of the SP and their ground truths on other projected planes was also satisfactory. More importantly, the difference in mean curvatures was slight on all projected planes between tracking results and ground truths. Thus, this study effectively demonstrates the promising potential of our 3-D spinal profile extraction method for the 3-D measurement of spinal deformities using 3-D ultrasound data.

Predictive analysis of the scoliotic curve using a subject's 3D model

A predictive analysis of the conservative scoliosis treatment is necessary, in which a 3D model of an optimal treatment algorithm is a basic part in the design of a prosthetic corset. Since CAD technology has proven to be very useful in the field of prosthetics and orthotics, we used an open-source software to plan the correction of the scoliotic curve on a virtual model of the subject's torso. The shape of the scoliosis was simplified by means of a directional polygon, which was drawn in a reverse manner depending on the directional arcs of the scoliotic curve. The resulting scoliosis correction, simulated in a predictive analysis, was defined by changing the Cobb angle, eccentricity, and torso height. With the proposed low-cost method of predictive analysis, it is possible to help CPOs to a more accurate and effective design of orthoses and corrective aids and to comprehensively determine the entire treatment procedure.

Automatic 3-D spine curve measurement in freehand ultrasound via structure-aware reinforcement learning spinous process localization

Freehand 3-D ultrasound systems have been advanced in scoliosis assessment to avoid radiation hazards, especially for teenagers. This novel 3-D imaging method also makes it possible to evaluate the spine curvature automatically from the corresponding 3-D projection images. However, most approaches neglect the three-dimensional spine deformity by only using the rendering images, thus limiting their usage in clinical applications. In this study, we proposed a structure-aware localization model to directly identify the spinous processes for automatic 3-D spine curve measurement using the images acquired with freehand 3-D ultrasound imaging. The pivot is to leverage a novel reinforcement learning (RL) framework to localize the landmarks, which adopts a multi-scale agent to boost structure representation with positional information. We also introduced a structure similarity prediction mechanism to perceive the targets with apparent spinous process structures. Finally, a two-fold filtering strategy was proposed to screen the detected spinous processes landmarks iteratively, followed by a three-dimensional spine curve fitting for the spine curvature assessments. We evaluated the proposed model on 3-D ultrasound images among subjects with different scoliotic angles. The results showed that the mean localization accuracy of the proposed landmark localization algorithm was 5.95 pixels. Also, the curvature angles on the coronal plane obtained by the new method had a high linear correlation with those by manual measurement (R = 0.86, p < 0.001). These results demonstrated the potential of our proposed method for facilitating the 3-D assessment of scoliosis, especially for 3-D spine deformity assessment.

Comparison of ultrasound scanning for scoliosis assessment: Robotic versus manual

BACKGROUND

Ultrasound (US) imaging for scoliosis assessment is challenging for a non-experienced operator. The robotic scanning was developed to follow a spinal curvature with deep learning and apply consistent forces to the patient's back.

METHODS

Twenty three scoliosis patients were scanned with US device both, robotically and manually. Two human raters measured each subject's spinous process angles on robotic and manual coronal images.

RESULTS

The robotic method showed high intra- (ICC > 0.85) and inter-rater (ICC > 0.77) reliabilities. Compared with the manual method, the robotic approach showed no significant difference (p < 0.05) when measuring coronal deformity angles. The mean absolute deviation for intra-rater analysis lies within an acceptable range from 0 to 5° for the minimum of 86% and maximum 97% of a total number of the measured angles.

CONCLUSIONS

This study demonstrated that scoliosis deformity angles measured on ultrasound images obtained with robotic scanning are comparable to those obtained by manual scanning.

Assessment of thoracic spinal curvatures in static postures using spatially tracked 3D ultrasound volumes: a proof-of-concept study

The assessment of spinal posture is a difficult endeavour given the lack of identifiable bony landmarks for placement of skin markers. Moreover, potentially significant soft tissue artefacts along the spine further affect the accuracy of marker-based approaches. The objective of this proof-of-concept study was to develop an experimental framework to assess spinal postures by using three-dimensional (3D) ultrasound (US) imaging. A phantom spine model immersed in water was scanned using 3D US in a neutral and two curved postures mimicking a forward flexion in the sagittal plane while the US probe was localised by three electromagnetic tracking sensors attached to the probe head. The obtained anatomical 'coarse' registrations were further refined using an automatic registration algorithm and validated by an experienced sonographer. Spinal landmarks were selected in the US images and validated against magnetic resonance imaging data of the same phantom through image registration. Their position was then related to the location of the tracking sensors identified in the acquired US volumes, enabling the localisation of landmarks in the global coordinate system of the tracking device. Results of this study show that localised 3D US enables US-based anatomical reconstructions comparable to clinical standards and the identification of spinal landmarks in different postures of the spine. The accuracy in sensor identification was 0.49 mm on average while the intra- and inter-observer reliability in sensor identification was strongly correlated with a maximum deviation of 0.8 mm. Mapping of landmarks had a small relative distance error of 0.21 mm (SD = ± 0.16) on average. This study implies that localised 3D US holds the potential for the assessment of full spinal posture by accurately and non-invasively localising vertebrae in space.

Ultrasound to X-ray synthesis generative attentional network (UXGAN) for adolescent idiopathic scoliosis

Standing X-ray radiograph with Cobb's method is the gold standard for scoliosis diagnosis. However, radiation hazard restricts its application, especially for close follow-up of adolescent patients. Compared with X-ray, ultrasound imaging has advantages of being radiation-free and real-time. To combine advantages of the above two imaging modalities, an ultrasound to X-ray synthesis generative attentional network (UXGAN) was proposed to synthesize ultrasound images into X-ray-like images. In this network, a cyclically consistent network was adopted and was trained end-to-end. An attention module was added and different residual blocks were designed. The quantitative comparison results demonstrated the superiority of our method to the state-of-the-art CycleGAN methods. We further compared the Cobb angle values measured on synthesized images and the real X-ray images, respectively. A good linear correlation (r = 0.95) was demonstrated between the two methods. The above results proved that the proposed method is of great significance for providing both X-ray images and ultrasound images based on the radiation-free ultrasound scanning.

Retrospective Analysis of FED Method Treatment Results in 11-17-Year-Old Children with Idiopathic Scoliosis

(1) Background: Idiopathic scoliosis is a major treatment problem due to its unknown origin and its three-dimensional nature. Attempts to cure it and search for new methods of physiotherapeutic treatment that would lead to its correction are one of the key issues of modern medicine. One of them is the fixation, elongation, de-rotation method (FED), used in the conservative treatment of idiopathic scoliosis. The aim of the study was evaluation of the short-term effectiveness of the FED method in the treatment of patients with idiopathic scoliosis. (2) Methods: Each patient underwent therapy based on the guidelines of the FED method. Patients were tested with the Bunnell scoliometer and the Zebris computer system. The treatment period was three weeks, after which the examinations were repeated. (3) Results: The results appeared to be statistically significant for all tested variables. (4) Conclusions: The examinations showed that the FED method had a statistically significant effect on the improvement of all parameters of posture examination, regardless of the size of the scoliotic deformation angle and bone maturity.

CT analysis of the posterior anatomical landmarks of the scoliotic spine

AIM

To use computed tomography (CT) to assess the validity and reliability of the posterior landmarks, spinous processes (SP), transverse processes (TP), and centre of lamina (COL), as compared to the Cobb angle to assess the curve severity and progression of adolescent idiopathic scoliosis (AIS).

MATERIALS AND METHODS

A consecutive series of CT examinations of severe AIS patients were included retrospectively. SP, TP, and COL angles were measured for all curves and compared to the Cobb angle.

RESULTS

One hundred and five patients were included. The mean Cobb versus SP, TP, and COL angles were, 54° versus 37°, 49°, and 51° in the thoracic curves and 34° versus 26°, 31°, and 34° in the (thoraco)lumbar curves. Intraclass correlation coefficient values for intra-rater measurements of the SP, TP, and COL angles were 0.93, 0.97, and 0.95 and 0.70, 0.90, and 0.88 for inter-rater measurements. The correlations between the Cobb angle and SP, TP, and COL angles in thoracic and (thoraco)lumbar curves were 0.79 and 0.66, 0.87 and 0.84, and 0.80 and 0.70.

CONCLUSIONS

The posterior spinal landmarks can be used for assessment of scoliosis severity in AIS; however, they show a systematic underestimation, but a strong correlation with the coronal Cobb angle. TP and COL angles had the highest validity.

Joint Spine Segmentation and Noise Removal From Ultrasound Volume Projection Images With Selective Feature Sharing

Volume Projection Imaging from ultrasound data is a promising technique to visualize spine features and diagnose Adolescent Idiopathic Scoliosis. In this paper, we present a novel multi-task framework to reduce the scan noise in volume projection images and to segment different spine features simultaneously, which provides an appealing alternative for intelligent scoliosis assessment in clinical applications. Our proposed framework consists of two streams: i) A noise removal stream based on generative adversarial networks, which aims to achieve effective scan noise removal in a weakly-supervised manner, i.e., without paired noisy-clean samples for learning; ii) A spine segmentation stream, which aims to predict accurate bone masks. To establish the interaction between these two tasks, we propose a selective feature-sharing strategy to transfer only the beneficial features, while filtering out the useless or harmful information. We evaluate our proposed framework on both scan noise removal and spine segmentation tasks. The experimental results demonstrate that our proposed method achieves promising performance on both tasks, which provides an appealing approach to facilitating clinical diagnosis.

A real-time freehand 3D ultrasound imaging method for scoliosis assessment

Real‐time 3D ultrasound has gained popularity in many fields because it can provide interactive feedback to help acquire high‐quality images or to conduct timely diagnosis. However, no comprehensive study has been reported on such an imaging method for scoliosis evaluation due to the complexity of this application. Meanwhile, the use of radiation‐free assessment of scoliosis is becoming increasingly popular. This study developed a real‐time 3D ultrasound imaging method for scoliosis assessment based on an incremental imaging method. In vivo experiments involving 36 patients with scoliosis were performed to test the performance of the proposed method. This new imaging method achieved a mean incremental frame rate of 82.7 ± 11.0 frames/s. The high repeatability of the intra‐operator test (intraclass correlation coefficient [ICC] = 0.92) and inter‐operator test (ICC = 0.91) demonstrated that the new method was very reliable. The result of spinous process angles obtained by the new method was linearly correlated (y = 0.97x, R² = 0.88) with that obtained by conventional 3D reconstruction. These results suggested that the newly developed imaging method can provide real‐time ultrasound imaging for scoliosis evaluation while preserving the comparative image quality of the conventional 3D reconstruction method.

Spine Posture, Mobility, and Stability of Top Mobile Esports Athletes: A Case Series

Professional esports athletes spend a long time in the same sitting posture during training and competition. Mobile esports may exacerbate potential postural problems because of the closer and unsupported arms and because athletes spend more time in a forward-/flexed-head posture. Prolonged sitting in these postures carries significant health risks and may lead to musculoskeletal problems and injuries. The objective of this retrospective study is to assess the posture, mobility, and stability of the spine for professional mobile esports athletes. We collected spine-assessment data from 48 athletes participating in a top-tier league on a real-time-strategy battle-arena online game. The spinal assessment was conducted using the SpinalMouse® under upright standing and trunk flexion in addition to the Matthiass test. Measurements were converted into Idiag Scores by the SpinalMouse® software. The Idiag Posture, Idiag Mobility, and Idiag Stability scores were 62.50 (IQR: 21), 63.50 (IQR: 19.5), and 54.50 (IQR: 14.5), respectively, and were significantly lower (p < 0.001) than the reference normative value (100). Age was found to have a weak positive correlation with the posture score (ρ = 0.29, p = 0.048). Although career duration appeared to lower the scores, the association was insignificant (p > 0.05). The scores also had no significant association with body height, body mass, body mass index, and esports team (p > 0.05). It was anticipated that mobile-based esports would attenuate the biomechanics of the spine and increase the likelihood of musculoskeletal problems, such as neck and back pain.

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

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.

Semi-automatic ultrasound curve angle measurement for adolescent idiopathic scoliosis

PURPOSE

Using X-ray to evaluate adolescent idiopathic scoliosis (AIS) conditions is the clinical gold standard, with potential radiation hazards. 3D ultrasound has demonstrated its validity and reliability of estimating X-ray Cobb angle (XCA) using spinous process angle (SPA), which can be automatically measured. While angle measurement with ultrasound using spine transverse process-related landmarks (UCA) shows better agreed with XCA, its automatic measurement is challenging and not available yet. This research aimed to analyze and measure scoliotic angles through a novel semi-automatic UCA method.

METHODS

100 AIS subjects (age: 15.0 ± 1.9 years, gender: 19 M and 81 F, Cobb: 25.5 ± 9.6°) underwent both 3D ultrasound and X-ray scanning on the same day. Scoliotic angles with XCA and UCA methods were measured manually; and transverse process-related features were identified/drawn for the semi-automatic UCA method. The semi-automatic method measured the spinal curvature with pairs of thoracic transverse processes and lumbar lumps in respective regions.

RESULTS

The new semi-automatic UCA method showed excellent correlations with manual XCA (R² = 0.815: thoracic angles R² = 0.857, lumbar angles R² = 0.787); and excellent correlations with manual UCA (R² = 0.866: thoracic angles R² = 0.921, lumbar angles R² = 0.780). The Bland-Altman plot also showed a good agreement against manual UCA/XCA. The MADs of semi-automatic UCA against XCA were less than 5°, which is clinically insignificant.

CONCLUSION

The semi-automatic UCA method had demonstrated the possibilities of estimating manual XCA and UCA. Further advancement in image processing to detect the vertebral landmarks in ultrasound images could help building a fully automated measurement method.

LEVEL OF EVIDENCE

Level III.

Effectiveness of the FED Method in the Treatment of Idiopathic Scoliosis of Girls Aged 11-15 Years

(1) Background: The unknown etiology of idiopathic scoliosis and its three-dimensional nature make the cause-and-effect therapeutic management difficult. A tendency to progression of scoliosis and the failure of many methods of conservative treatment have prompted the search for new methods that would stop and correct deformations. One of them is the FED method, used in the conservative treatment of idiopathic scolioses, in which all scoliotic curves are corrected. The aim of this study was a comparative analysis of the effectiveness of idiopathic scoliosis treatment with the FED and FITS methods. (2) Methods: The study included 60 randomly selected girls, aged 11 to 15 years, treated with the FED and FITS methods. They were diagnosed with idiopathic scoliosis grade II according to Cobb and double-curve scoliosis type I and II according to King–Moe classification. The results of the therapy were assessed with the use of the Bunnell scoliometer. The examinations were performed before the start of the therapy—on the first day of the child’s stay—and 3 weeks after the therapy. The angle of trunk rotation and the sum of two rotations were assessed using a scoliometer. (3) Results: The performed statistical analysis demonstrated significant changes in the examined parameters in both therapeutic groups. (4) Conclusions: 1. The obtained results indicate that the FED therapy may prove to be an effective method of treating idiopathic scoliosis; however, it requires further research in a larger group of patients; 2. both methods significantly improved trunk rotation in primary and secondary scoliosis, but after using summing parameters (SDR parameter), the FED method appeared to be statistically more effective.

Light-Convolution Dense Selection U-Net (LDS U-Net) for Ultrasound Lateral Bony Feature Segmentation

Scoliosis is a widespread medical condition where the spine becomes severely deformed and bends over time. It mostly affects young adults and may have a permanent impact on them. A periodic assessment, using a suitable modality, is necessary for its early detection. Conventionally, the usually employed modalities include X-ray and MRI, which employ ionising radiation and are expensive. Hence, a non-radiating 3D ultrasound imaging technique has been developed as a safe and economic alternative. However, ultrasound produces low-contrast images that are full of speckle noise, and skilled intervention is necessary for their processing. Given the prevalent occurrence of scoliosis and the limitations of scalability of human expert interventions, an automatic, fast, and low-computation assessment technique is being developed for mass scoliosis diagnosis. In this paper, a novel hybridized light-weight convolutional neural network architecture is presented for automatic lateral bony feature identification, which can help to develop a fully-fledged automatic scoliosis detection system. The proposed architecture, Light-convolution Dense Selection U-Net (LDS U-Net), can accurately segment ultrasound spine lateral bony features, from noisy images, thanks to its capabilities of smartly selecting only the useful information and extracting rich deep layer features from the input image. The proposed model is tested using a dataset of 109 spine ultrasound images. The segmentation result of the proposed network is compared with basic U-Net, Attention U-Net, and MultiResUNet using various popular segmentation indices. The results show that LDS U-Net provides a better segmentation performance compared to the other models. Additionally, LDS U-Net requires a smaller number of parameters and less memory, making it suitable for a large-batch screening process of scoliosis without a high computational requirement.

Improvement of 3-D Ultrasound Spine Imaging Technique Using Fast Reconstruction Algorithm

Three-dimensional (3-D) freehand ultrasound (US) imaging has been applied to the investigation of spine deformity. However, it is a challenge for the current 3-D imaging reconstruction algorithms to achieve a balance between image quality and computation time. The objectives of this article are to implement a new fast reconstruction algorithm that can fulfill the request of immediate demonstration and processing for high-quality 3-D spine imaging, and to evaluate the reliability and accuracy of scoliotic curvature measurement when using the algorithm. The fast dot-projection (FDP) algorithm was applied for voxel-based nearest neighbor (VNN), multiple plane interpolation (MPI), and pixel nearest neighbor (PNN) protocols to reduce the reconstruction time. The 3-D image volume was reconstructed from the datasets acquired from scoliotic subjects. The computational cost, image characteristics, and statistical analyses of curve measurements were compared and evaluated among different reconstruction protocols. The results illustrated that the 3-D spine images using the FDP-MPI4 algorithm showed higher brightness (20%), contrast (14%), and signal-to-noise ratio (SNR) (26%) than FDP-VNN. The measurement performed by trainee rater exhibited significant improvement in measurement reliability and accuracy using FDP-MPI4 in comparison with FDP-VNN ( ), and the intraclass correlation coefficient (ICC) of interrater measurement increased from 0.88 to 0.96. The FDP-PNN method could acquire and reconstruct spine images simultaneously and present the results in 1-2 min, which showed the potential to provide the approximate real-time visualization for fast screening.

Using Ultrasound to Screen for Scoliosis to Reduce Unnecessary Radiographic Radiation: A Prospective Diagnostic Accuracy Study on 442 Schoolchildren

Scoliosis screening is important for timely initiation of brace treatment to mitigate curve progression in skeletally immature children and adolescents. School scoliosis screening programs in Hong Kong follow the protocol of referring children screened positive with a scoliometer and Moiré topography for confirmatory standard radiography. Despite being highly sensitive (88%) in detecting those who require specialist referral, the screening program was found to have a false-positive rate >50%, which could lead to unnecessary X-ray radiation. Radiation-free ultrasound has been reported to be valid and reliable for quantitative assessment of curve severity in scoliosis patients. The aim of this prospective diagnostic accuracy study was to determine the accuracy of ultrasound in determining the threshold of referral that requires X-ray for children screened positive with the scoliometer and Moiré topography. Our study recruited 442 schoolchildren with a mean Cobb angle of 14.0 ± 6.6°. The sensitivity and specificity of ultrasound in predicting the correct referral status, confirmed by X-ray, were 92.3% and 51.6%, with positive and negative predictive values of 29.0% and 96.9%, respectively. Receiver operating characteristic curve analysis revealed area under the curve values of 0.735 for ultrasound alone and 0.832 for ultrasound in combination with measurement of angle of trunk rotation. The finding supports the accuracy of using ultrasound to determine referral status, which could result in a >50% reduction of unnecessary radiation for children undergoing scoliosis screening.

3D ultrasound imaging provides reliable angle measurement with validity comparable to X-ray in patients with adolescent idiopathic scoliosis

BACKGROUND & OBJECTIVE

The application of ultrasound imaging for spine evaluation could minimize radiation exposure for patients with adolescence idiopathic scoliosis (AIS). A customized three-dimensional (3D) ultrasound imaging system has been demonstrated to provide reliable and valid coronal curvature measurements. However, these measurements were using the spinous processes as anatomical reference, leading to a predictable underestimation of the traditionally used Cobb angles. An alternative 3D ultrasound image reconstruction method was applied to create coronal images with more lateral features for angle measurement. The objective of this study was to test the reliability and the validity of this angle, the ultrasound curve angle (UCA), and compare the UCA with the Cobb angles on X-ray images of patients with AIS.

MATERIALS AND METHODS

This study was divided into: 1) Investigation of intra- and inter-reliability between two raters for measuring the UCA and two operators for acquiring ultrasound images; 2) Investigation of the validity between the radiographic Cobb angle and the UCA. Fifty patients and 164 patients with AIS, were included in the two stages, respectively. Patients underwent bi-planar X-ray and 3D ultrasound scanning on the same day. The proposed UCA was used to measure the coronal curvature from the ultrasound coronal images, which were formed using a newly customized volume projection imaging (VPI) method. The intra-rater/operator and inter-rater and operator reliability of the UCA were tested by intra-class correlation coefficient (ICC) (3,1) and (2,1), respectively. The validity of UCA measurements as compared to radiographic Cobb angles was tested by inter-method ICC (2,1), mean absolute difference (MAD), standard error of measurement (SEM), Pearson correlation coefficient and Bland-Altman statistics. The level of significance was set as 0.05.

RESULTS

Excellent intra-rater and intra-operator (ICC (3,1)≥0.973) and excellent inter-rater and inter-operator reliability (ICC (2,1)≥0.925) for UCA measurement, with overall MAD and SEM no more than 3.5° and 1.7° were demonstrated for both main thoracic and (thoraco)lumbar curvatures. Very good correlations were observed between UCA and Cobb angle for main thoracic (R 2 =0.893) and (thoraco)lumbar (R 2 =0.884) curves. The mean (SD) measurements in terms of radiographic Cobb and UCA were 27.2 ​± ​11.6° and 26.3 ​± ​11.4° for main thoracic curves; and 26.2 ​± ​11.4° and 24.8 ​± ​9.7° for (thoraco)lumbar curve respectively. One hundred sixty-four subjects (33 male and 131 female subjects; 11-18 years of age, mean of 15.1 ​± ​1.9 years) were included for the validity session. Excellent inter-method variations (ICC (2,K) ≥0.933) with overall MAD and SEM no more than 3.0° and 1.5° were demonstrated for both main thoracic and (thoraco)lumbar curvatures. In addition, Bland-Altman plots demonstrated an acceptable agreement between ultrasound and radiographic Cobb measurements.

CONCLUSION

In this study, very good correlations and agreement were demonstrated between the ultrasound and X-ray measurements of the scoliotic curvature. Judging from the promising results of this study, patients with AIS with different severity of curves can be evaluated and monitored by ultrasound imaging, reducing the usage of radiation during follow-ups. This method could also be used for scoliosis screening.The Translational potential of this article: Ultrasound curve angle (UCA) obtained from 3D ultrasound imaging system can provide reliable and valid evaluation on coronal curvature for patients with AIS, without the need of radiation.

Automatic Detection and Measurement of Spinous Process Curve on Clinical Ultrasound Spine Images

The ultrasound (US) imaging technique has been applied to scoliosis assessment, and the proxy Cobb angle can be acquired on the US coronal images. The spinous process angle (SPA) is a valuable parameter to indicate 3-D deformity of spine. However, the SPA cannot be measured on US images since the spinous process (SP) is merged in the soft tissue layer and impossible to be identified on the coronal view directly. A new method based on the gradient vector flow (GVF) snake model was proposed to automatically locate SP position on the US transverse images, and the density-based spatial clustering of application with noise (DBSCAN) was used to remove the outliers out of the detected location results. With marking the SP points on the US coronal image, the SP curve was interpolated and the SPA was measured. The algorithm was evaluated on 50 subjects with various severity of scoliosis, and two raters measured the SPA on both US images and radiographs manually. The mean absolute differences (MADs) of SPAs obtained from the two modalities were 3.4° ± 2.4° and 3.6° ± 2.8° for the two raters, respectively, which were less than the clinical acceptance error (5°), and the results reported a good linear correlation ( ) between the US method and radiography. It indicates that the proposed method can be a promising approach for SPA measurement using the US imaging technique.

Validation of Scolioscan Air-Portable Radiation-Free Three-Dimensional Ultrasound Imaging Assessment System for Scoliosis

To diagnose scoliosis, the standing radiograph with Cobb's method is the gold standard for clinical practice. Recently, three-dimensional (3D) ultrasound imaging, which is radiation-free and inexpensive, has been demonstrated to be reliable for the assessment of scoliosis and validated by several groups. A portable 3D ultrasound system for scoliosis assessment is very much demanded, as it can further extend its potential applications for scoliosis screening, diagnosis, monitoring, treatment outcome measurement, and progress prediction. The aim of this study was to investigate the reliability of a newly developed portable 3D ultrasound imaging system, Scolioscan Air, for scoliosis assessment using coronal images it generated. The system was comprised of a handheld probe and tablet PC linking with a USB cable, and the probe further included a palm-sized ultrasound module together with a low-profile optical spatial sensor. A plastic phantom with three different angle structures built-in was used to evaluate the accuracy of measurement by positioning in 10 different orientations. Then, 19 volunteers with scoliosis (13F and 6M; Age: 13.6 ± 3.2 years) with different severity of scoliosis were assessed. Each subject underwent scanning by a commercially available 3D ultrasound imaging system, Scolioscan, and the portable 3D ultrasound imaging system, with the same posture on the same date. The spinal process angles (SPA) were measured in the coronal images formed by both systems and compared with each other. The angle phantom measurement showed the measured angles well agreed with the designed values, 59.7 ± 2.9 vs. 60 degrees, 40.8 ± 1.9 vs. 40 degrees, and 20.9 ± 2.1 vs. 20 degrees. For the subject tests, results demonstrated that there was a very good agreement between the angles obtained by the two systems, with a strong correlation (R2 = 0.78) for the 29 curves measured. The absolute difference between the two data sets was 2.9 ± 1.8 degrees. In addition, there was a small mean difference of 1.2 degrees, and the differences were symmetrically distributed around the mean difference according to the Bland-Altman test. Scolioscan Air was sufficiently comparable to Scolioscan in scoliosis assessment, overcoming the space limitation of Scolioscan and thus providing wider applications. Further studies involving a larger number of subjects are worthwhile to demonstrate its potential clinical values for the management of scoliosis.

Dual-task ultrasound spine transverse vertebrae segmentation network with contour regularization

3D ultrasound imaging has become one of the common diagnosis ways to assess scoliosis since it is radiation-free, real-time, and low-cost. Spine curvature angle measurement is an important step to assess scoliosis precisely. One way to calculate the angle is using the vertebrae features of the 2-D coronal images to identify the most tilted vertebrae. To do the measurement, the segmentation of the transverse vertebrae is an important step. In this paper, we propose a dual-task ultrasound transverse vertebrae segmentation network (D-TVNet) based on U-Net. First, we arrange an auxiliary shape regularization network to learn the contour segmentation of the bones. It improves the boundary segmentation and anti-interference ability of the U-Net by fusing some of the features of the auxiliary task and the main task. Then, we introduce the atrous spatial pyramid pooling (ASPP) module to the end of the down-sampling stage of the main task stream to improve the relative feature extraction ability. To further improve the boundary segmentation, we extendedly fuse the down-sampling output features of the auxiliary network in the ASPP. The experiment results show that the proposed D-TVNet achieves the best dice score of 86.68% and the mean dice score of 86.17% based on cross-validation, which is an improvement of 5.17% over the baseline U-Net. An automatic ultrasound spine bone segmentation network with promising results has been achieved.

Ultrasound volume projection image quality selection by ranking from convolutional RankNet

Periodic inspection and assessment are important for scoliosis patients. 3D ultrasound imaging has become an important means of scoliosis assessment as it is a real-time, cost-effective and radiation-free imaging technique. With the generation of a 3D ultrasound volume projection spine image using our Scolioscan system, a series of 2D coronal ultrasound images are produced at different depths with different qualities. Selecting a high quality image from these 2D images is the crucial task for further scoliosis measurement. However, adjacent images are similar and difficult to distinguish. To learn the nuances between these images, we propose selecting the best image automatically, based on their quality rankings. Here, the ranking algorithm we use is a pairwise learning-to-ranking network, RankNet. Then, to extract more efficient features of input images and to improve the discriminative ability of the model, we adopt the convolutional neural network as the backbone due to its high power of image exploration. Finally, by inputting the images in pairs into the proposed convolutional RankNet, we can select the best images from each case based on the output ranking orders. The experimental result shows that convolutional RankNet achieves better than 95.5% top-3 accuracy, and we prove that this performance is beyond the experience of a human expert.

Cross-validation of ultrasound imaging in adolescent idiopathic scoliosis

PURPOSE

Adolescent idiopathic scoliosis (AIS) patients are exposed to 9-10 times more radiation and a fivefold increased lifetime cancer risk. Radiation-free imaging alternatives are needed. Ultrasound imaging of spinal curvature was shown to be accurate, however, systematically underestimating the Cobb angle. The purpose of this study is to create and cross-validate an equation that calculates the expected Cobb angle using ultrasound spinal measurements of AIS patients.

METHODS

Seventy AIS patients with upright radiography and spinal ultrasound were split randomly in a 4:1 ratio to the equation creation (n = 54) or validation (n = 16) group. Ultrasound angles based on the spinous processes shadows were measured automatically by the ultrasound system (Scolioscan, Telefield, Hong Kong). For thoracic and lumbar curves separately, the equation: expected Cobb angle = regression coefficient × ultrasound angle, was created and subsequently cross-validated in the validation group.

RESULTS

Linear regression analysis between ultrasound angles and radiographic Cobb angles (thoracic: R² = 0.968, lumbar: R² = 0.923, p < 0.001) in the creation group resulted in the equations: thoracic Cobb angle = 1.43 × ultrasound angle and lumbar Cobb angle = 1.23 × ultrasound angle. With these equations, expected Cobb angles in the validation group were calculated and showed an excellent correlation with the radiographic Cobb angles (thoracic: R² = 0.959, lumbar: R² = 0.936, p < 0.001). The mean absolute differences were 6.5°-7.3°. Bland-Altman plots showed good accuracy and no proportional bias.

CONCLUSION

The equations from ultrasound measurements to Cobb angles were valid and accurate. This supports the implementation of ultrasound imaging, possibly leading to less frequent radiography and reducing ionizing radiation in AIS patients.

A freehand ultrasound framework for spine assessment in 3D: a preliminary study

X-ray imaging is currently the gold standard for the assessment of spinal deformities. The purpose of this study is to evaluate a freehand 3D ultrasound system for volumetric reconstruction of the spine. A setup consisting of an ultrasound scanner with a linear transducer, an electromagnetic measuring system and a workstation was used. We conducted 64 acquisitions of US images of 8 adults in a natural standing position, and we tested three setups: 1) Subjects are constrained to be close to a wall, 2) Subjects are unconstrained, and 3) Subjects are constrained to performing fast and slow acquisitions. The spinous processes were manually selected from the volume reconstruction from tracked ultrasound images to generate a 3D point-based model depicting the centerline of the spine. The results suggested that a freehand 3D ultrasound system can be suitable for representing the spine. Volumetric reconstructions can be computed and landmarking can be performed to model the surface of the spine in the 3D space. These reconstructions promise to generate computer-based descriptors to analyze the shape of the spine in the 3D space.Clinical Relevance- We provide clinicians with a protocol that could be integrated in clinical setups for the assessment and monitoring of AIS, based on US image acquisitions, which constitutes a radiation-free technology.

3D Ultrasound Spine Image Selection Using Convolution Learning-to-Rank Algorithm

3D Ultrasound imaging has become an important means of scoliosis assessment as it is a real-time, cost-effective and radiation-free imaging technique. However, the coronal images from different depths of a 3D ultrasound image have different imaging definitions. So there is a need to select the coronal image that would give the best image definition. Also, manual selection of coronal images is time-consuming and limited to the discretion and capability of the assessor. Therefore, in this paper, we have developed a convolution learning-to-rank algorithm to select the best ultrasound images automatically using raw ultrasound images. The ranking is done based on the curve angle of the spinal cord. Firstly, we approached the image selection problem as a ranking problem; ranked based on probability of an image to be a good image. Here, we use the RankNet, a pairwise learning-to-rank method, to rank the images automatically. Secondly, we replaced the backbone of the RankNet, which is the traditional artificial neural network (ANN), with convolution neural network (CNN) to improve the feature extracting ability for the successive iterations. The experimental result shows that the proposed convolutional RankNet achieves the perfect accuracy of 100% while conventional DenseNet achieved 35% only. This proves that the convolutional RankNet is more suitable to highlight the best quality of ultrasound image from multiple mediocre ones.

Modeling and Analysis of Ultrasound Elastographic Axial Strains for Spine Fracture Identification

This study reports the first use of ultrasound (US) elastography for imaging spinal fractures by assessing the mechanical response of the soft tissue at the posterior vertebra boundary to a uniaxial compression in rabbit ex vivo samples. Three-dimensional finite-element (FE) models of the vertebra-soft tissue complex in rabbit samples are generated and analyzed to evaluate the distribution of the axial normal and shear strains at the vertebra-soft tissue interface. Experiments on the same samples are performed to corroborate simulation findings. Results of this study indicate that the distribution of the axial strains manifests as distinct patterns around intact and fractured vertebrae. Numerical characteristics of the axial strain's spatial distribution are further used to construct two shape descriptors to make inferences on spinal abnormalities: 1) axial normal strain asymmetry for assessing the presence of fractures and 2) principal orientation of axial shear strain concentration regions (shear zones) for measurement of spinous process dislocation. This study demonstrates that axial normal strain and axial shear strain maps obtained via US elastography can provide a new means to detect spine fractures and abnormalities in the selected ex vivo animal models. Spinal fracture detection is important for the assessment of spinal cord injuries and stability. However, identification of spinal fractures using US is currently challenging. Our results show that features resulting from strain elastograms can serve as a useful adjunct to B-mode images in identifying spine fractures in the selected animal samples, and this information could be helpful in clinical settings.

Automatic Spine Ultrasound Segmentation for Scoliosis Visualization and Measurement

OBJECTIVE

Integrate tracked ultrasound and AI methods to provide a safer and more accessible alternative to X-ray for scoliosis measurement. We propose automatic ultrasound segmentation for 3-dimensional spine visualization and scoliosis measurement to address difficulties in using ultrasound for spine imaging.

METHODS

We trained a convolutional neural network for spine segmentation on ultrasound scans using data from eight healthy adult volunteers. We tested the trained network on eight pediatric patients. We evaluated image segmentation and 3-dimensional volume reconstruction for scoliosis measurement.

RESULTS

As expected, fuzzy segmentation metrics reduced when trained networks were translated from healthy volunteers to patients. Recall decreased from 0.72 to 0.64 (8.2% decrease), and precision from 0.31 to 0.27 (3.7% decrease). However, after finding optimal thresholds for prediction maps, binary segmentation metrics performed better on patient data. Recall decreased from 0.98 to 0.97 (1.6% decrease), and precision from 0.10 to 0.06 (4.5% decrease). Segmentation prediction maps were reconstructed to 3-dimensional volumes and scoliosis was measured in all patients. Measurement in these reconstructions took less than 1 minute and had a maximum error of 2.2° compared to X-ray.

CONCLUSION

automatic spine segmentation makes scoliosis measurement both efficient and accurate in tracked ultrasound scans.

SIGNIFICANCE

Automatic segmentation may overcome the limitations of tracked ultrasound that so far prevented its use as an alternative of X-ray in scoliosis measurement.

Automating Spine Curvature Measurement in Volumetric Ultrasound via Adaptive Phase Features

Ultrasound volume projection imaging (VPI) has been recently suggested. This novel imaging method allows a non-radiation assessment of spine deformity with free-hand 3-D ultrasound imaging. This paper presents a fully automatic method to evaluate the spine curve in VPI images corresponding to different projection depth of the volumetric ultrasound, thus making it possible to analyze 3-D spine deformity. The new automatic method is based on prior knowledge about the geometric arrangement of the spinous processes. The frequency bandwidth of log-Gabor filters is adaptively adjusted to calculate the oriented phase congruency, facilitating the segmentation of the spinous column profile. And the spine curvature angle is finally calculated according to the inflection points of the curve over the segmented spinous column profile. The performance of the automatic method is evaluated on spine VPI images among patients with different scoliotic angles. The curvature angles obtained using the proposed method have a high linear correlation with those by the manual method (r = 0.90, p < 0.001) and X-ray Cobb's method (r = 0.87, p < 0.001). The feasibility of 3-D spine deformity assessment is also demonstrated using VPI images corresponding to various projection depth. The results suggest that this method can substantially improve the recognition of the spinous column profile, especially facilitating the applications of 3-D spine deformity assessment.

Evaluation of Scoliosis With a Commercially Available Ultrasound System

OBJECTIVES

Currently, radiography with measurement of the Cobb angle is still considered the reference standard for diagnosing scoliosis. However, the ionizing radiation hazard is drawing wide attention. Can 3-dimensional (3D) ultrasound (US) be an alternative modality for diagnosing and monitoring patients with scoliosis? The aim of our study was to assess the reliability and validity of 3D US imaging in the evaluation of scoliosis.

METHODS

A commercially available ultrasound system with a magnetic tracking system was selected for long-distance 3D US imaging. Straight phantoms and curved phantoms were scanned with the imaging system to evaluate the stability of the system for curvature measurements. Eight healthy adult volunteers and 28 patients with scoliosis were recruited for long-distance 3D US imaging. The intraclass correlation coefficient was used to test the reproducibility of the interobserver and intraobserver measurements for both the healthy adults and patients with scoliosis. A linear regression analysis and Bland-Altman plot were used to analyze the correlation and to determine the extent of agreement between the angles measured on US images and the Cobb angles measured on conventional radiographs.

RESULTS

The 28 patients with scoliosis included 10 male and 18 female patients aged 8 to 37 years (mean age ± SD, 17.7 ± 1.4 years; body mass index, <25 kg/m² ). In the phantom study, there was no statistically significant difference between the angles measured by the 3D US imaging system and those measured by an angle gauge (P > 0.05). In the clinical study, there was very good interobserver and intraobserver reliability (intraclass correlation coefficients, >0.90) for the US imaging system, with a high correlation (r² = 0.92) and agreement between the US and radiographic methods.

CONCLUSIONS

The long-distance 3D US imaging system offers a viable modality for diagnosing and monitoring scoliosis without radiation.

Is Radiation-Free Ultrasound Accurate for Quantitative Assessment of Spinal Deformity in Idiopathic Scoliosis (IS): A Detailed Analysis With EOS Radiography on 952 Patients

Radiation exposure with repeated radiography required at follow-up poses serious health concerns for scoliosis patients. Although spinous process angle (SPA) measurement of spinal curvatures with ultrasound has been reported with promising results, an evidence-based account on its accuracy for translational application remains undefined. This prospective study involved 952 idiopathic scoliosis patients (75.7% female, mean age 16.7 ± 3.0 y, Cobb 28.7 ± 11.6°). Among 1432 curves (88.1%) detected by ultrasound, there was good correlation between radiologic Cobb angles measured manually on EOS (E_Cobb) whole-spine radiographs and automatic ultrasound SPA measurement for upper spinal curves (USCs) (r = 0.873, apices T7-T12/L1 intervertebral disc) and lower spinal curves (LSCs) (r = 0.740, apices L1 or below) (p < 0.001). Taller stature was associated with stronger correlation. For E_Cobb <30°, 66.6% USCs and 62.4% LSCs had absolute differences between E_Cobb and predicted Cobb angle calculated from SPA ≤5°. Ultrasound could be a viable option in lieu of radiography for measuring coronal curves with apices at T7 or lower and Cobb angle <30°.

A semi-automatic 3D ultrasound reconstruction method to assess the true severity of adolescent idiopathic scoliosis

Adolescent idiopathic scoliosis (AIS) is a three-dimensional (3D) spinal deformity. Current practice uses the Cobb method to measure spinal severity on postero-anterior (PA) radiographs. This method may underestimate spinal deformity and exposes patients to ionizing radiation, increasing the risk of cancer. This paper reports a new 3D ultrasound method using the voxel-based reconstruction technique with bilinear interpolation to reconstruct a 3D spinal image and measure true spinal curvature on the plane of maximal curvature (PMC). Axial vertebral rotation (AVR) was measured on the 3D image and utilized to estimate the PMC. In vitro phantom experiments and in vivo clinical study were conducted to evaluate reconstruction accuracy and measurement reliability. The in vitro study showed a high accuracy of the reconstruction of vertebrae with the mean absolute difference (MAD) < 3 mm. The in vitro and in vivo measurements of AVR were reliable (> 0.90). The in vivo study also showed high intra- and inter-rater reliabilities of the PA and PMC Cobb angle measurements with ICC values > 0.90 and MADs within the clinical accepted tolerances. The PMC Cobb angles were up to 7° greater than their corresponding PA Cobb angles. This method demonstrated a non-ionizing radiation method to assess the actual severity of AIS. Graphical abstract Adolescent idiopathic scoliosis (AIS) is a lateral curvature of spine with vertebral rotation. Using the Cobb method to measure spinal severity on postero-anterior (PA) radiographs may under estimate its severity.