Direct Estimation of Spinal Cobb Angles by Structured Multi-Output Regression

• URL: http://arxiv.org/abs/2012.12626v1
• Date: Wed, 23 Dec 2020 12:33:46 GMT
• Title: Direct Estimation of Spinal Cobb Angles by Structured Multi-Output Regression
• Authors: Haoliang Sun, Xiantong Zhen, Chris Bailey, Parham Rasoulinejad, Yilong Yin, Shuo Li
• Abstract summary: The Cobb angle that quantitatively evaluates the spinal curvature plays an important role in the scoliosis diagnosis and treatment. We formulate the estimation of the Cobb angles from spinal X-rays as a multi-output regression task. Our method achieves the direct estimation of Cobb angles with high accuracy, which indicates its great potential in clinical use.
• Score: 42.67503464183464
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The Cobb angle that quantitatively evaluates the spinal curvature plays an important role in the scoliosis diagnosis and treatment. Conventional measurement of these angles suffers from huge variability and low reliability due to intensive manual intervention. However, since there exist high ambiguity and variability around boundaries of vertebrae, it is challenging to obtain Cobb angles automatically. In this paper, we formulate the estimation of the Cobb angles from spinal X-rays as a multi-output regression task. We propose structured support vector regression (S^2VR) to jointly estimate Cobb angles and landmarks of the spine in X-rays in one single framework. The proposed S^2VR can faithfully handle the nonlinear relationship between input images and quantitative outputs, while explicitly capturing the intrinsic correlation of outputs. We introduce the manifold regularization to exploit the geometry of the output space. We propose learning the kernel in S2VR by kernel target alignment to enhance its discriminative ability. The proposed method is evaluated on the spinal X-rays dataset of 439 scoliosis subjects, which achieves the inspiring correlation coefficient of 92.76% with ground truth obtained manually by human experts and outperforms two baseline methods. Our method achieves the direct estimation of Cobb angles with high accuracy, which indicates its great potential in clinical use.

Related papers

• SG-LRA: Self-Generating Automatic Scoliosis Cobb Angle Measurement with Low-Rank Approximation [38.35450156563293]
  We propose a framework including Self-Generation pipeline and Low-Rank Approximation representation (SG-LRA) for automatic Cobb angle measurement. Specifically, we propose a parameterized spine contour representation based on LRA, which enables eigen-spine decomposition and spine contour reconstruction. With our data engine, we generate the largest scoliosis X-ray dataset named Spinal-AI2024 largely without privacy leaks.
  arXiv  Detail & Related papers  (2024-11-19T16:07:58Z)
• Deep learning automates Cobb angle measurement compared with multi-expert observers [3.7153471185088427]
  The Cobb angle is a widely used scoliosis quantification method that measures the degree of curvature between the tilted vertebrae. We have created fully automated software that precisely measures the Cobb angle and provides clear visualizations of these measurements. This software integrates deep neural network-based spine region detection and segmentation, spine centerline identification, pinpointing the most significantly tilted vertebrae.
  arXiv  Detail & Related papers  (2024-03-18T15:43:45Z)
• Predicting Spine Geometry and Scoliosis from DXA Scans [49.68543422441626]
  We first train a neural network to predict the middle spine curve in the scan, and then use an integral-based method to determine the curvature along the spine curve. We show that the maximum curvature can be used as a scoring function for ordering the severity of spinal deformation.
  arXiv  Detail & Related papers  (2023-11-15T22:49:08Z)
• B-Spine: Learning B-Spline Curve Representation for Robust and Interpretable Spinal Curvature Estimation [50.208310028625284]
  We propose B-Spine, a novel deep learning pipeline to learn B-spline curve representation of the spine. We estimate the Cobb angles for spinal curvature estimation from low-quality X-ray images.
  arXiv  Detail & Related papers  (2023-10-14T15:34:57Z)
• Semantic Latent Space Regression of Diffusion Autoencoders for Vertebral Fracture Grading [72.45699658852304]
  This paper proposes a novel approach to train a generative Diffusion Autoencoder model as an unsupervised feature extractor. We model fracture grading as a continuous regression, which is more reflective of the smooth progression of fractures. Importantly, the generative nature of our method allows us to visualize different grades of a given vertebra, providing interpretability and insight into the features that contribute to automated grading.
  arXiv  Detail & Related papers  (2023-03-21T17:16:01Z)
• Development of Machine learning algorithms to identify the Cobb angle in adolescents with idiopathic scoliosis based on lumbosacral joint efforts during gait (Case study) [1.1199585259018454]
  The aim of this study is to identify the Cobb angle by developing an automated radiation-free model. The lumbosacral joint efforts during gait as radiation-free data are capable to identify the Cobb angle.
  arXiv  Detail & Related papers  (2023-01-29T23:58:16Z)
• Automating Cobb Angle Measurement for Adolescent Idiopathic Scoliosis using Instance Segmentation [1.3161405778899375]
  Currently, the reference standard for assessing scoliosis is based on the manual assignment of Cobb angles. This paper proposes to address the Cobb angle measurement task using YOLACT, an instance segmentation model. The proposed method first segments the vertebrae in an X-Ray image using YOLACT, then it tracks the important landmarks using the minimum bounding box approach.
  arXiv  Detail & Related papers  (2022-11-25T14:04:06Z)
• Simultaneous Estimation of X-ray Back-Scatter and Forward-Scatter using Multi-Task Learning [59.17383024536595]
  Back-scatter significantly contributes to patient (skin) dose during complicated interventions. Forward-scattered radiation reduces contrast in projection images and introduces artifacts in 3-D reconstructions. We propose a novel approach combining conventional techniques with learning-based methods to simultaneously estimate the forward-scatter reaching the detector.
  arXiv  Detail & Related papers  (2020-07-08T10:47:37Z)
• Analysis of Scoliosis From Spinal X-Ray Images [17.8260780895433]
  Measurement of scoliosis requires labeling and identification of vertebrae in the spine. Scoliosis is a congenital disease in which the spine is deformed from its normal shape. We propose an end-to-end segmentation model that provides a fully automatic and reliable segmentation of the vertebrae associated with scoliosis measurement.
  arXiv  Detail & Related papers  (2020-04-15T05:36:28Z)
• Vertebra-Focused Landmark Detection for Scoliosis Assessment [54.24477530836629]
  We propose a novel vertebra-focused landmark detection method. Our model first localizes the vertebra centers, based on which it then traces the four corner landmarks of the vertebra through the learned corner offset. Results demonstrate the merits of our method in both Cobb angle measurement and landmark detection on low-contrast and ambiguous X-ray images.
  arXiv  Detail & Related papers  (2020-01-09T19:17:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.