Automatic bony structure segmentation and curvature estimation on ultrasound cervical spine images - a feasibility study
2024
会议录名称JOURNAL OF PHYSICS: CONFERENCE SERIES
ISSN1742-6588
卷号2822
期号1
发表状态已发表
DOI10.1088/1742-6596/2822/1/012023
摘要

The loss of cervical lordosis is a common degenerative disorder known to be associated with abnormal spinal alignment. In recent years, ultrasound (US) imaging has been widely applied in the assessment of spine deformity and has shown promising results. The objectives of this study are to automatically segment bony structures from the 3D US cervical spine image volume and to assess the cervical lordosis on the key sagittal frames. In this study, a portable ultrasound imaging system was applied to acquire cervical spine image volume. The nnU-Net was trained on to segment bony structures on the transverse images and validated by 5-fold-cross-validation. The volume data were reconstructed from the segmented image series. An energy function indicating intensity levels and integrity of bony structures was designed to extract the proxy key sagittal frames on both left and right sides for the cervical curve measurement. The mean absolute difference (MAD), standard deviation (SD) and correlation between the spine curvatures of the left and right sides were calculated for quantitative evaluation of the proposed method. The DSC value of the nnU-Net model in segmenting ROI was 0.973. For the measurement of 22 lamina curve angles, the MAD±SD and correlation between the left and right sides of the cervical spine were 3.591±3.432◦ and 0.926, respectively. The results indicate that our method has a high accuracy and reliability in the automatic segmentation of the cervical spine and shows the potential of diagnosing the loss of cervical lordosis using the 3D ultrasound imaging technique. © 2024 Institute of Physics Publishing. All rights reserved.

关键词Image segmentation Ultrasonics Bony structures Cervical lordosis Cervical spine Curvature estimation Feasibility studies Image volume Mean absolute differences Standard correlation Standard deviation Structure segmentation
会议名称2023 International Congress on Ultrasonics, ICU Beijing 2023
会议地点Beijing, China
会议日期September 18, 2023 - September 21, 2023
收录类别EI
语种英语
出版者Institute of Physics
EI入藏号20244617348021
EI主题词Ultrasonic imaging
EISSN1742-6596
EI分类号1106.3.1 ; 746 Imaging Techniques ; 753.1 Ultrasonic Waves ; 753.3 Ultrasonic Applications
原始文献类型Conference article (CA)
文献类型会议论文
条目标识符https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/449150
专题信息科学与技术学院
信息科学与技术学院_PI研究组_郑锐组
信息科学与技术学院_硕士生
信息科学与技术学院_本科生
通讯作者Zheng, Rui
作者单位
1.School of Information Science and Technology, ShanghaiTech University, Shanghai; 201210, China
2.Shanghai Engineering Research Center of Energy Efficient and Custom AI, Shanghai, Shanghai; 201210, China
第一作者单位信息科学与技术学院
通讯作者单位信息科学与技术学院
第一作者的第一单位信息科学与技术学院
推荐引用方式
GB/T 7714
Ge, Songhan,Tian, Haoyuan,Zhang, Wei,et al. Automatic bony structure segmentation and curvature estimation on ultrasound cervical spine images - a feasibility study[C]:Institute of Physics,2024.
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