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

Liu, Juan; Bi, Xiaojun; Banerjee, Sunetra; Huang, Zixun; Leung, F.H.F.; Lee, Timothy Tin-Yan; Yang, Deyou; Zheng, Yong; Ling, Sai Ho

doi:10.1016/j.compmedimag.2021.101896

Cited by 11 publications

(10 citation statements)

References 20 publications

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“…Application of deep learning using neural network could be a potential solution to make these processes more effective in clinical routine in future studies. 42,43 Small sample size was one of the limitations of this study. In addition, although AVR severity was demonstrated to have no significant effect on the variation of the differences between the 3D ultrasonography and EOS AVR measurements, the curves involved in this study were not large enough (12.0-55.3 ); therefore, the applicability of 3D ultrasonography to evaluate AVR of patients with severe scoliosis is yet to be investigated.…”

Section: Discussionmentioning

confidence: 93%

“…Nevertheless, both 3D spine reconstruction from EOS radiographs and acquisition of spinal parameters from 3D ultrasonography involve intensive training and knowledge about the anatomy of spine that are time consuming. Application of deep learning using neural network could be a potential solution to make these processes more effective in clinical routine in future studies 42,43 …”

Section: Discussionmentioning

confidence: 99%

“…Application of deep learning using neural network could be a potential solution to make these processes more effective in clinical routine in future studies. 42 , 43 …”

Section: Discussionmentioning

confidence: 99%

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Three‐dimensional ultrasonography could be a potential non‐ionizing tool to evaluate vertebral rotation of subjects with adolescent idiopathic scoliosis

et al. 2023

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BackgroundThree‐dimensional (3D) ultrasonography is nonionizing and has been demonstrated to be a reliable tool for scoliosis assessment, including coronal and sagittal curvatures. It shows a great potential for axial vertebral rotation (AVR) evaluation, yet its validity and reliability need to be further demonstrated.Materials and MethodsTwenty patients with adolescent idiopathic scoliosis (AIS) (coronal Cobb: 26.6 ± 9.1°) received 3D ultrasound scan for twice, 10 were scanned by the same operator, and the other 10 by different operators. EOS Bi‐planar x‐rays and 3D scan were conducted on another 29 patients on the same day. Two experienced 3D ultrasonographic researchers, with different experiences on AVR measurement, evaluated the 3D ultrasonographic AVR of the 29 patients (55 curves; coronal Cobb angle: 26.9 ± 11.3°). The gold standard AVR was determined from the 3D reconstruction of coronal and sagittal EOS radiographs. Intra‐class correlation coefficients (ICCs), mean absolute difference (MAD), standard error measurements (SEM), and Bland–Altman's bias were reported to evaluate the intra‐operator and inter‐operator/rater reliabilities of 3D ultrasonography. The reliability of 3D ultrasonographic AVR measurements was further validated using inter‐method with that of EOS.ResultsICCs for intra‐operator and inter‐operator/rater reliability assessment were all greater than 0.95. MAD, SEM, and bias for the 3D ultrasonographic AVRs were no more than 2.2°, 2.0°, and 0.5°, respectively. AVRs between both modalities were strongly correlated (R2 = 0.901) and not significantly different (p = 0.205). Bland–Altman plot also shows that the bias was less than 1°, with no proportional bias between the difference and mean of expected and radiographic Cobb angles.ConclusionThis study demonstrates that 3D ultrasonography is valid and reliable to evaluate AVR in AIS patients. 3D ultrasonography can be a potential tool for screening and following up subjects with AIS and evaluating the effectiveness of nonsurgical treatments.

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Section: Discussionmentioning

confidence: 93%

Section: Discussionmentioning

confidence: 99%

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Three‐dimensional ultrasonography could be a potential non‐ionizing tool to evaluate vertebral rotation of subjects with adolescent idiopathic scoliosis

et al. 2023

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“…The SVseg model also achieved the best results compared with well-known vertebrae segmentation methods. For example, a mean 86.17% DICE score was reported for vertebrae segmentation using (D-TVNet) based on U-Net [68]. The experimental results showed that D-TVNet was unable to determine the critical points for measuring the spine curve angle using segmented bones.…”

Section: Discussionmentioning

confidence: 99%

SVseg: Stacked Sparse Autoencoder-Based Patch Classification Modeling for Vertebrae Segmentation

et al. 2022

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Precise vertebrae segmentation is essential for the image-related analysis of spine pathologies such as vertebral compression fractures and other abnormalities, as well as for clinical diagnostic treatment and surgical planning. An automatic and objective system for vertebra segmentation is required, but its development is likely to run into difficulties such as low segmentation accuracy and the requirement of prior knowledge or human intervention. Recently, vertebral segmentation methods have focused on deep learning-based techniques. To mitigate the challenges involved, we propose deep learning primitives and stacked Sparse autoencoder-based patch classification modeling for Vertebrae segmentation (SVseg) from Computed Tomography (CT) images. After data preprocessing, we extract overlapping patches from CT images as input to train the model. The stacked sparse autoencoder learns high-level features from unlabeled image patches in an unsupervised way. Furthermore, we employ supervised learning to refine the feature representation to improve the discriminability of learned features. These high-level features are fed into a logistic regression classifier to fine-tune the model. A sigmoid classifier is added to the network to discriminate the vertebrae patches from non-vertebrae patches by selecting the class with the highest probabilities. We validated our proposed SVseg model on the publicly available MICCAI Computational Spine Imaging (CSI) dataset. After configuration optimization, our proposed SVseg model achieved impressive performance, with 87.39% in Dice Similarity Coefficient (DSC), 77.60% in Jaccard Similarity Coefficient (JSC), 91.53% in precision (PRE), and 90.88% in sensitivity (SEN). The experimental results demonstrated the method’s efficiency and significant potential for diagnosing and treating clinical spinal diseases.

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“…As a result, segmentation performance has improved. Another method D-TVNet [57] based on U-Net obtained a mean DSC of 86.17%. The experiment's findings demonstrate that D-TVNet cannot identify the essential spots for measuring the angle of the spine curve when segmented bones are used.…”

Section: Discussionmentioning

confidence: 99%

OP-convNet: A Patch Classification-Based Framework for CT Vertebrae Segmentation

et al. 2021

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Accurate vertebrae segmentation from medical images plays an important role in clinical tasks of surgical planning, diagnosis, kyphosis, scoliosis, degenerative disc disease, spondylolisthesis, and postoperative assessment. Although the structures of bone have high contrast in medical images, vertebrae segmentation is a challenging task due to its complex structure, abnormal spine curves, and unclear boundaries. In recent years, deep learning has been widely applied in the segmentation of vertebrae images. In this paper, towards a robust and automatic segmentation system, we present an overlapping patch-based convNet (OP-convNet) model for automatic vertebrae CT images segmentation. Due to the greater memory and processing costs associated with 3D convolutional neural networks, as well as the risk of over-fitting, we employ overlapping patches in segmentation tasks using 2D convNet. In the proposed vertebrae segmentation method, OP-convNet effectively keeps the local information contained in CT images. We divide CT image slices into equal-sized square overlapping patches and applied the RUS-function on these patches for class balancing to minimize computational requirements. Then, these patches are input into the model along with their corresponding ground truth patches. This method has been evaluated on publicly available CT images from the MICCAI CSI workshop challenge. The results indicate that OP-convNet has precision (PRE) of 90.1%, specificity (SPE) of 99.4%, accuracy (ACC) of 98.8%, F-score of 90.1% in terms of the patch-based classification accuracy, and BF-score of 90.2%, sensitivity (SEN) of 90.3%, Jaccard index (JAC) of 82.3%, dice similarity score (DSC) of 89.9% in terms of the segmentation accuracy that outperform previous methods across all metrics.

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

Cited by 11 publications

References 20 publications

Three‐dimensional ultrasonography could be a potential non‐ionizing tool to evaluate vertebral rotation of subjects with adolescent idiopathic scoliosis

Three‐dimensional ultrasonography could be a potential non‐ionizing tool to evaluate vertebral rotation of subjects with adolescent idiopathic scoliosis

SVseg: Stacked Sparse Autoencoder-Based Patch Classification Modeling for Vertebrae Segmentation

OP-convNet: A Patch Classification-Based Framework for CT Vertebrae Segmentation

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