Evidence map›Paper›PMID 38568402›Full record

ArticleInternational journal of computer assisted radiology and surgery2024

Incremental regression of localization context for automatic segmentation of ossified ligamentum flavum from CT data.

Rong Tao, Xiaoyang Zou, Xiaoru Gao, Xinhua Li, Zhiyu Wang, Xin Zhao, Guoyan Zheng, Donghua Hang

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Article in International journal of computer assisted radiology and surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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Authors and funding

8 authors.

Rong TaoInstitute of Medical Robotics, Shanghai Jiao Tong University, Dongchuan Road, Shanghai, 200240, China.
Xiaoyang ZouInstitute of Medical Robotics, Shanghai Jiao Tong University, Dongchuan Road, Shanghai, 200240, China.
Xiaoru GaoInstitute of Medical Robotics, Shanghai Jiao Tong University, Dongchuan Road, Shanghai, 200240, China.
Xinhua LiDepartment of Orthopedics, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China.
Zhiyu WangDepartment of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China.
Xin ZhaoDepartment of Orthopedics, Shanghai 9th People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China. zhaoxinmlg@126.com.
Guoyan ZhengInstitute of Medical Robotics, Shanghai Jiao Tong University, Dongchuan Road, Shanghai, 200240, China. guoyan.zheng@sjtu.edu.cn.ORCID http://orcid.org/0000-0003-4173-0379
Donghua HangDepartment of Orthopedics, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China. donghua.hang@shsmu.edu.cn.

Funding

National Natural Science Foundation of China U20A20199
6 · The paper itself

Abstract

purposeSegmentation of ossified ligamentum flavum (OLF) plays a crucial role in developing computer-assisted, image-guided systems for decompressive thoracic laminectomy. Manual segmentation is time-consuming, tedious, and label-intensive. It also suffers from inter- and intra-observer variability. Automatic segmentation is highly desired.

methodsA two-stage, localization context-aware framework is developed for automatic segmentation of ossified ligamentum flavum. In the first stage, localization heatmaps of OLFs are obtained via incremental regression. In the second stage, the obtained heatmaps are then treated as the localization context for a segmentation U-Net. Our framework can directly map a whole volumetic data to its volume-wise labels.

resultsWe designed and conducted comprehensive experiments on datasets of 100 patients to evaluate the performance of the proposed method. Our method achieved an average Dice similarity coefficient of 61.2 ± 7.6%, an average surface distance of 1.1 ± 0.5 mm, and an average positive predictive value of 62.0 ± 12.8%.

conclusionTo the best knowledge of the authors, this is the first study aiming for automatic segmentation of ossified ligamentum flavum. Results from the comprehensive experiments demonstrate the superior performance of the proposed method over the state-of-the-art methods.

Indexed as

Ligamentum FlavumOssification, HeterotopicTomography, X-Ray ComputedDecompression, SurgicalHumansLaminectomyThoracic VertebraeContext-guidedIncremental regressionOssification of the ligament flavumSegmentation

Identifiers

PMID38568402

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