Proceedings ArticleDOI
Fully-Automated Analysis of Scoliosis from Spinal X-Ray Images
Abdullah-Al-Zubaer Imran,Chao Huang,Hui Tang,Wei Fan,Kenneth M.C. Cheung,Michael To,Zhen Qian,Demetri Terzopoulos +7 more
- pp 114-119
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TLDR
An end-to-end spine radiograph analysis pipeline that automatically provides an accurate segmentation and identification of the vertebrae, culminating in the reliable estimation of the Cobb angle, the most widely used measurement to quantify the magnitude of scoliosis.Abstract:
Scoliosis is a congenital disease in which the spine is deformed from its normal shape. Radiography is the most cost-effective and accessible modality for imaging the spine. Conventional spinal assessment, diagnosis of scoliosis, and treatment planning relies on tedious and time-consuming manual analysis of spine radiographs that is susceptible to observer variation. A reliable, fully-automated method that can accurately identify vertebrae, a crucial step in image-guided scoliosis assessment, is presently unavailable in the literature. Leveraging a novel, deep-learning-based image segmentation model, we develop an end-to-end spine radiograph analysis pipeline that automatically provides an accurate segmentation and identification of the vertebrae, culminating in the reliable estimation of the Cobb angle, the most widely used measurement to quantify the magnitude of scoliosis. Our experimental results with anterior-posterior spine X-ray images indicate that our system is effective in the identification and labeling of vertebrae, and can potentially provide assistance to medical practitioners in the assessment of scoliosis.read more
Citations
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Proceedings ArticleDOI
A Deep Learning Approach for Automatic Scoliosis Cobb Angle Identification
Renato R. Maaliw,Julie Ann B. Susa,Alvin Sarraga Alon,Ace C. Lagman,Shaneth C. Ambat,Manuel Barrera García,Keno C. Piad,M. C. F. Raguro +7 more
TL;DR: This study proposed an end-to-end pipeline for automatic Cobb angle measurement to pinpoint scoliosis severity using convolutional neural networks, showing that the Residual U-Net architecture provides vertebrae average segmentation accuracy based on Dice and Jaccard similarity coefficients.
Book ChapterDOI
Joint Learning with Local and Global Consistency for Improved Medical Image Segmentation
TL;DR: In this paper , a joint patch-and image-level training framework was proposed to improve the segmentation performance of U-Net, UNet++, and NodeU-Net.
Proceedings ArticleDOI
Semi-automated estimation of spinal curvature from scoliosis radiographs using difference matrix
TL;DR: In this paper, a semi-automated approach for accurate estimation of Cobb angles from scoliosis radiographs using a difference matrix is proposed, given the landmark points of the vertebral columns in the spine, the spinal midline is determined by curve fitting of the 4th degree polynomial.
Journal ArticleDOI
Survey of Advances in Cobb Angle Measurement for Automatic Spine Detection in X-Ray
TL;DR: Using RGB and complexity photos collected by an RGB-complexity device Microsoft, a modified convolutional network (MCN) named fuse-Unet is the proposal to provide automatic recognition of the human spine area and which was before the imaging route as mentioned in this paper .
References
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Book ChapterDOI
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Posted Content
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Journal ArticleDOI
Adolescent idiopathic scoliosis.
TL;DR: Adolescent idiopathic scoliosis is a common problem; its prevalence in the general population is about 1.8 percent, if minor curvatures of 5 to 10 degrees are included.
Journal ArticleDOI
Scoliosis imaging: What Radiologists should know
Hana Kim,Hak Sun Kim,Eun Su Moon,Choon Sik Yoon,Tae Sub Chung,Ho Taek Song,Jin Suck Suh,Young Han Lee,Sungjun Kim +8 more
TL;DR: In idiopathic scoliosis, progression is most likely during periods of rapid growth, and the optimal follow-up interval in skeletally immature patients may be as short as 4 months, so only curves of more than 30° must be monitored for progression.