人工智能
稳健性(进化)
可解释性
曲率
分割
计算机科学
花键(机械)
深度学习
柯布角
模式识别(心理学)
B样条曲线
脊柱侧凸
计算机视觉
数学
工程类
几何学
医学
数学分析
生物化学
化学
结构工程
外科
基因
作者
Hao Wang,Qiang Song,Rong Yin,Rui Ma,Yizhou Yu,Yi Chang
出处
期刊:Cornell University - arXiv
日期:2023-10-14
标识
DOI:10.48550/arxiv.2310.09603
摘要
Spinal curvature estimation is important to the diagnosis and treatment of the scoliosis. Existing methods face several issues such as the need of expensive annotations on the vertebral landmarks and being sensitive to the image quality. It is challenging to achieve robust estimation and obtain interpretable results, especially for low-quality images which are blurry and hazy. In this paper, we propose B-Spine, a novel deep learning pipeline to learn B-spline curve representation of the spine and estimate the Cobb angles for spinal curvature estimation from low-quality X-ray images. Given a low-quality input, a novel SegRefine network which employs the unpaired image-to-image translation is proposed to generate a high quality spine mask from the initial segmentation result. Next, a novel mask-based B-spline prediction model is proposed to predict the B-spline curve for the spine centerline. Finally, the Cobb angles are estimated by a hybrid approach which combines the curve slope analysis and a curve-based regression model. We conduct quantitative and qualitative comparisons with the representative and SOTA learning-based methods on the public AASCE2019 dataset and our new proposed CJUH-JLU dataset which contains more challenging low-quality images. The superior performance on both datasets shows our method can achieve both robustness and interpretability for spinal curvature estimation.
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