Automatic measurement algorithm of scoliosis Cobb angle based on deep learning

Tu, Yongcheng; Wang, Nian; Tong, Fei; Chen, Hemu

doi:10.1088/1742-6596/1187/4/042100

Cited by 24 publications

(14 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In 2019, Yongcheng 18 and his team attempt to extract the Spine contour through deep learning approach in the feature map plane. The features used in their study includes gradient magnitude and gradient histogram.…”

Section: Related Workmentioning

confidence: 99%

Computerized image understanding system for reliable estimation of spinal curvature in idiopathic scoliosis

Thalengala

Bhat

Anitha

2021

Sci Rep

View full text Add to dashboard Cite

Analysis of scoliosis requires thorough radiographic evaluation by spinal curvature estimation to completely assess the spinal deformity. Spinal curvature estimation gives orthopaedic surgeons an idea of severity of spinal deformity for therapeutic purposes. Manual intervention has always been an issue to ensure accuracy and repeatability. Computer assisted systems are semi-automatic and is still influenced by surgeon’s expertise. Spinal curvature estimation completely relies on accurate identification of required end vertebrae like superior end-vertebra, inferior end-vertebra and apical vertebra. In the present work, automatic extraction of spinal information central sacral line and medial axis by computerized image understanding system has been proposed. The inter-observer variability in the anatomical landmark identification is quantified using Kappa statistic. The resultant Kappa value computed between proposed algorithm and observer lies in the range 0.7 and 0.9, which shows good accuracy. Identification of the required end vertebra is automated by the extracted spinal information. Difference in inter and intra-observer variability for the state of the art computer assisted and proposed system are quantified in terms of mean absolute difference for the various types (Type-I, Type-II, Type-III, Type-IV, and Type-V) of scoliosis.

show abstract

Section: Related Workmentioning

confidence: 99%

Computerized image understanding system for reliable estimation of spinal curvature in idiopathic scoliosis

Thalengala

Bhat

Anitha

2021

Sci Rep

View full text Add to dashboard Cite

show abstract

“…In recent years, there has been an increased interest in computer‐aided scoliosis evaluation, most of which achieved bias within 5 degrees 15–28,41,43,44 . However, to date, few deep‐learning researches have been involved in the assessment of the cervical spine region.…”

Section: Discussionmentioning

confidence: 99%

“…The segmentation‐based method mainly processes the segmentation as a first‐step task to crop landmark regions and then calculates the Cobb angle based on preceding results. Tu et al 15 . and Zhao et al 16 .…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

View full text Add to dashboard Cite

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.

show abstract

“…Studies have assessed the use of deep learning algorithms (DLAs) in the detection of scoliosis. Tan et al ( 11 ) and Tu et al ( 20 ) applied a deep learning-based system to diagnose scoliosis on spine X-ray images. They found that DLAs can achieve segmentation of lumbar spine radiographs and automatic measurement of the Cobb angle.…”

Section: Discussionmentioning

confidence: 99%

Diagnosis of Scoliosis Using Chest Radiographs with a Semi-Supervised Generative Adversarial Network

Lee

Shin

Lee

et al. 2022

J Korean Soc Radiol

View full text Add to dashboard Cite

Purpose To develop and validate a deep learning-based screening tool for the early diagnosis of scoliosis using chest radiographs with a semi-supervised generative adversarial network (GAN). Materials and Methods Using a semi-supervised learning framework with a GAN, a screening tool for diagnosing scoliosis was developed and validated through the chest PA radiographs of patients at two different tertiary hospitals. Our proposed method used training GAN with mild to severe scoliosis only in a semi-supervised manner, as an upstream task to learn scoliosis representations and a downstream task to perform simple classification for differentiating between normal and scoliosis states sensitively. Results The area under the receiver operating characteristic curve, negative predictive value (NPV), positive predictive value, sensitivity, and specificity were 0.856, 0.950, 0.579, 0.985, and 0.285, respectively. Conclusion Our deep learning-based artificial intelligence software in a semi-supervised manner achieved excellent performance in diagnosing scoliosis using the chest PA radiographs of young individuals; thus, it could be used as a screening tool with high NPV and sensitivity and reduce the burden on radiologists for diagnosing scoliosis through health screening chest radiographs.

show abstract

Automatic measurement algorithm of scoliosis Cobb angle based on deep learning

Cited by 24 publications

References 10 publications

Computerized image understanding system for reliable estimation of spinal curvature in idiopathic scoliosis

Computerized image understanding system for reliable estimation of spinal curvature in idiopathic scoliosis

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

Diagnosis of Scoliosis Using Chest Radiographs with a Semi-Supervised Generative Adversarial Network

Contact Info

Product

Resources

About