Vertebra-Focused Landmark Detection for Scoliosis Assessment
- URL: http://arxiv.org/abs/2001.03187v1
- Date: Thu, 9 Jan 2020 19:17:41 GMT
- Title: Vertebra-Focused Landmark Detection for Scoliosis Assessment
- Authors: Jingru Yi, Pengxiang Wu, Qiaoying Huang, Hui Qu, Dimitris N. Metaxas
- Abstract summary: 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.
- Score: 54.24477530836629
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Adolescent idiopathic scoliosis (AIS) is a lifetime disease that arises in
children. Accurate estimation of Cobb angles of the scoliosis is essential for
clinicians to make diagnosis and treatment decisions. The Cobb angles are
measured according to the vertebrae landmarks. Existing regression-based
methods for the vertebra landmark detection typically suffer from large dense
mapping parameters and inaccurate landmark localization. The segmentation-based
methods tend to predict connected or corrupted vertebra masks. In this paper,
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. In this way, our
method is able to keep the order of the landmarks. The comparison results
demonstrate the merits of our method in both Cobb angle measurement and
landmark detection on low-contrast and ambiguous X-ray images. Code is
available at: \url{https://github.com/yijingru/Vertebra-Landmark-Detection}.
Related papers
- Shape Matters: Detecting Vertebral Fractures Using Differentiable
Point-Based Shape Decoding [51.38395069380457]
Degenerative spinal pathologies are highly prevalent among the elderly population.
Timely diagnosis of osteoporotic fractures and other degenerative deformities facilitates proactive measures to mitigate the risk of severe back pain and disability.
In this study, we specifically explore the use of shape auto-encoders for vertebrae.
arXiv Detail & Related papers (2023-12-08T18:11:22Z) - 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) - 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) - Accurate Scoliosis Vertebral Landmark Localization on X-ray Images via
Shape-constrained Multi-stage Cascaded CNNs [14.298452017755197]
Vertebral landmark localization is a crucial step for variant spine-related clinical applications.
We propose multi-stage cascaded convolutional neural networks (CNNs) to split the single task into two sequential steps.
We evaluate our method on the AASCE dataset that consists of 609 tight spinal-posterior X-ray images.
arXiv Detail & Related papers (2022-06-05T02:45:40Z) - Stacked Hourglass Network with a Multi-level Attention Mechanism: Where
to Look for Intervertebral Disc Labeling [2.3848738964230023]
We propose a stacked hourglass network with multi-level attention mechanism to jointly learn intervertebral disc position and their skeleton structure.
The proposed deep learning model takes into account the strength of semantic segmentation and pose estimation technique to handle the missing area and false positive detection.
arXiv Detail & Related papers (2021-08-14T14:53:27Z) - Direct Estimation of Spinal Cobb Angles by Structured Multi-Output
Regression [42.67503464183464]
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.
arXiv Detail & Related papers (2020-12-23T12:33:46Z) - A Convolutional Approach to Vertebrae Detection and Labelling in Whole
Spine MRI [70.04389979779195]
We propose a novel convolutional method for the detection and identification of vertebrae in whole spine MRIs.
This involves using a learnt vector field to group detected vertebrae corners together into individual vertebral bodies.
We demonstrate the clinical applicability of this method, using it for automated scoliosis detection in both lumbar and whole spine MR scans.
arXiv Detail & Related papers (2020-07-06T09:37:12Z) - 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)
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.