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Lumbar spine localisation method based on feature fusion
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Zhang, Yonghong, Hu, Ning, Li, Zhuofu, Ji, Xuquan, Liu, Shanshan, Sha, Youyang, Song, Xiongkang, Zhang, Jian, Hu, Lei and Li, Weishi (2022) Lumbar spine localisation method based on feature fusion. CAAI Transactions on Intelligence Technology . doi:10.1049/cit2.12137 ISSN 2468-2322. (In Press)
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WRAP-Lumbar-spine-localisation-method-based-on-feature-fusion-Sha-2022.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (3474Kb) | Preview |
Official URL: http://dx.doi.org/10.1049/cit2.12137
Abstract
To eliminate unnecessary background information, such as soft tissues in original CT images and the adverse impact of the similarity of adjacent spines on lumbar image segmentation and surgical path planning, a two-stage approach for localising lumbar segments is proposed. First, based on the multi-scale feature fusion technology, a non-linear regression method is used to achieve accurate localisation of the overall spatial region of the lumbar spine, effectively eliminating useless background information, such as soft tissues. In the second stage, we directly realised the precise positioning of each segment in the lumbar spine space region based on the non-linear regression method, thus effectively eliminating the interference caused by the adjacent spine. The 3D Intersection over Union (3D_IOU) is used as the main evaluation indicator for the positioning accuracy. On an open dataset, 3D_IOU values of 0.8339 ± 0.0990 and 0.8559 ± 0.0332 in the first and second stages, respectively is achieved. In addition, the average time required for the proposed method in the two stages is 0.3274 and 0.2105 s respectively. Therefore, the proposed method performs very well in terms of both precision and speed and can effectively improve the accuracy of lumbar image segmentation and the effect of surgical path planning.
| Item Type: | Journal Article | |||||||||
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| Subjects: | R Medicine > RD Surgery | |||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||
| Library of Congress Subject Headings (LCSH): | Spine -- Imaging, Tomography, Spine -- Diseases -- Imaging, Lumbar vertebrae -- Diseases -- Diagnosis | |||||||||
| Journal or Publication Title: | CAAI Transactions on Intelligence Technology | |||||||||
| Publisher: | Wiley | |||||||||
| ISSN: | 2468-2322 | |||||||||
| Official Date: | 2022 | |||||||||
| Dates: |
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| DOI: | 10.1049/cit2.12137 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | In Press | |||||||||
| Access rights to Published version: | Open Access (Creative Commons) | |||||||||
| Date of first compliant deposit: | 19 October 2022 | |||||||||
| Date of first compliant Open Access: | 21 October 2022 | |||||||||
| RIOXX Funder/Project Grant: |
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