Automatic Spine Curvature Estimation by a Top-Down Approach

Zhao, Shixuan; Wang, Bo; Yang, Kai-Fu; Li, Yongjie

doi:10.1007/978-3-030-39752-4_8

Cited by 7 publications

(9 citation statements)

References 10 publications

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“…Our method is compared with various state-of -theart methods 2,3,5,6,[8][9][10]13,[16][17][18][19]37 using the same AASCE MICCAI 2019 challenge database (see Table 1). Lin et al 2 ranks the first when using EfficientNet_B1 as their backbone, whose SMAPE is the lowest (only 7.32%).…”

Section: Discussionmentioning

confidence: 99%

“…However, it is too tricky for neural networks to evaluate Cobb angles in X-rays automatically without auxiliaries because of the similar morphological appearance of vertebrae. To address this problem, loads of researchers use landmarks and segmentation areas to assist in calculating Cobb angles, so most studies 2,3,[5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] can be divided into two ways: landmark-based estimation methods 5,6,[8][9][10][11][12][13][14][15][16] and segmentation-based estimation methods. 2,3,7,[17][18][19][20] Landmark-based estimation methods normally first locate the four corners of each vertebra to estimate landmarks, and then compute Cobb angles through these landmarks.…”

Section: Introductionmentioning

confidence: 99%

“…A cascaded convolutional neural network was used to segment the spine and the centerline of the spine. 17 Lots of methods 2,[18][19][20] are similar to this method. Huang et al 7 proposed a segmentation-based method of the estimation of Cobb angles and the diagnosis of scoliosis.…”

Section: Introductionmentioning

confidence: 99%

“…However, it is too tricky for neural networks to evaluate Cobb angles in X‐rays automatically without auxiliaries because of the similar morphological appearance of vertebrae. To address this problem, loads of researchers use landmarks and segmentation areas to assist in calculating Cobb angles, so most studies 2,3,5‐20 can be divided into two ways: landmark‐based estimation methods 5,6,8‐16 and segmentation‐based estimation methods 2,3,7,17‐20 …”

Section: Introductionmentioning

confidence: 99%

“…To address this problem, loads of researchers use landmarks and segmentation areas to assist in calculating Cobb angles, so most studies 2,3,[5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] can be divided into two ways: landmark-based estimation methods 5,6,[8][9][10][11][12][13][14][15][16] and segmentation-based estimation methods. 2,3,7,[17][18][19][20] Landmark-based estimation methods normally first locate the four corners of each vertebra to estimate landmarks, and then compute Cobb angles through these landmarks. Yi et al 6 proposed a typical landmarkbased estimation methods, and they first located the vertebra centers, after which traced the four corner land-marks of the vertebra through the centers.…”

Section: Introductionmentioning

confidence: 99%

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W‐Transformer: Accurate Cobb angles estimation by using a transformer‐based hybrid structure

Yao

Gao

et al. 2022

Medical Physics

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Background: Scoliosis is a type of spinal deformity, which is harmful to a person's health. In severe cases, it can trigger paralysis or death. The measurement of Cobb angle plays an essential role in assessing the severity of scoliosis. Purpose: The aim of this paper is to propose an automatic system for landmark detection and Cobb angle estimation, which can effectively help clinicians diagnose and treat scoliosis. Methods: A novel hybrid framework was proposed to measure Cobb angle precisely for clinical diagnosis, which was referred as W-Transformer due to its wshaped architecture. First, a convolutional neural network of cascade residual blocks as our backbone was designed. Then a transformer was fused to learn the dependency information between spine and landmarks. In addition, a reinforcement branch was designed to improve the overlap of landmarks, and an improved prediction module was proposed to fine-tune the final coordinates of landmarks in Cobb angles estimation. Besides, the public Accurate Automated Spinal Curvature Estimation (AASCE) MICCAI 2019 challenge was served as data set. It supplies 609 manually labeled spine anterior-posterior (AP) X-ray images, each of which contains a total of 68 landmark labels and three Cobb Angles tags. Results: From the perspective of the AASCE MICCAI 2019 challenge, we achieved a lower symmetric mean absolute percentage error (SMAPE) of 8.26% for all Cobb angles and the lowest averaged detection error of 50.89 in terms of landmark detection, compared with many state-of -the-art methods. We also provided the SMAPEs for the Cobb angles of the proximal-thoracic (PT), the main-thoracic (MT), and the thoracic-lumbar (TL) area, which are 5.27%, 14.59%, and 20.97% respectively, however, these data were not covered in most previous studies. Statistical analysis demonstrates that our model has obtained a high level of Pearson correlation coefficient of 0.9398 (p < 0.001), which shows excellent reliability of our model. Our model can yield 0.9489 (p < 0.001), 0.8817 (p < 0.001), and 0.9149 (p < 0.001) for PT, MT, and TL, respectively. The overall variability of Cobb angle measurement is less than 4 • , implying clinical value. And the mean absolute deviation (standard deviation) for three regions is 3.64 • (4.13 • ), 3.84 • (4.66 • ), and 3.80 • (4.19 • ). The results of Student paired t-test indicate that no statistically significant differences are observed between manual measurement and our automatic approach (p-value is always >0.05). Regarding the diagnosis of scoliosis (Cobb angle >10 • ), the proposed method achieves a high sensitivity of 0.9577 and a specificity of 0.8475 for all spinal regions. Conclusions: This study offers a brand-new automatic approach that is potentially of great benefit of the complex task of landmark detection and Cobb angle 3246

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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W‐Transformer: Accurate Cobb angles estimation by using a transformer‐based hybrid structure

Yao

Gao

et al. 2022

Medical Physics

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Application of TVD‐Net for sagittal alignment and instability measurements in cervical spine radiographs

et al. 2023

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Background Cervical spinal malalignment and instability are frequently occurring pathological conditions involving neck pain, radiculopathy, and myelopathy, often requiring surgical intervention. Accurate assessment of cervical alignment and instability are essential in surgical planning and evaluating postoperative outcomes. Purpose To automatically measure the sagittal alignment and instability of the cervical spine, we develop a novel deep‐learning model by detecting landmarks on cervical radiographs. Methods We introduce the transformer‐embedded residual network (ResNet) as the network's core to automatically identify vertebral landmarks on digital and film‐transformed cervical radiographs, and simultaneously measure the segmental Cobb angle and horizontal displacement. A Transformer Module was embedded into the latent space to extract the relationship between different vertebrae. Then a Rotating Attention Module was integrated between the encoder‐decoder pairs to highlight the key points and maintain more details. Finally, a Vector Loss Module was proposed to restrain the orientation of the adjacent vertebra to reduce misdetection. All images were obtained from local hospital. Digital images were split into training, validation, and test subsets (896, 225, and 353 images, respectively). Likewise, film‐transformed images were split into 404, 115, and 150 images, respectively. The results of the model were compared with manual measurements. Results Our deep learning algorithm achieved mean absolute difference (MAD) at a level of 2.20° and 2.33°, symmetric mean absolute error(SMAPE)at 16.63% and 19.35%, respectively, when measuring Cobb angle on digital images and films. On evaluating cervical instability, the diagnostic accuracy, sensitivity, specificity, precision, and F1‐score evaluation metrics were calculated. The corresponding values were 89.80%, 86.49%, 90.68%, 71.11%, and 78.05% on digital images, and 90.00%, 83.78%, 91.15%, 75.61%, and 79.49% on film‐transformed images, which were comparable to experienced surgeons. Visualization results demonstrated robust effectiveness in subjects with severe osteophytes or artifacts. Conclusion This study presents a novel and efficient deep‐learning model to assist landmarks identification and angulation and displacement calculation on lateral cervical spine radiographs, and demonstrates excellent accuracy in measuring cervical alignment and sound sensitivity and specificity in cervical instability diagnosis. It should be helpful for future research and clinical applications.

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Joint Spinal Centerline Extraction and Curvature Estimation with Row-Wise Classification and Curve Graph Network

Huo

Cai

Liang

et al. 2021

Medical Image Computing and Computer Assisted Intervention – MICCAI 2021

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Automatic Spine Curvature Estimation by a Top-Down Approach

Cited by 7 publications

References 10 publications

W‐Transformer: Accurate Cobb angles estimation by using a transformer‐based hybrid structure

W‐Transformer: Accurate Cobb angles estimation by using a transformer‐based hybrid structure

Application of TVD‐Net for sagittal alignment and instability measurements in cervical spine radiographs

Joint Spinal Centerline Extraction and Curvature Estimation with Row-Wise Classification and Curve Graph Network

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