A Structure-Affinity Dual Attention-based Network to Segment Spine for Scoliosis Assessment
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2024, 00, pp. 1567-1574
- Issue Date:
- 2024-01-18
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Filename | Description | Size | |||
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1701318 am.pdf | Accepted version | 1.46 MB |
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Ultrasound volume projection imaging has shown great promise to visualize spine features and diagnose scoliosis thanks to its harmlessness cheapness and efficiency The key to measuring spine deformity and assessing scoliosis is to accurately segment the spine bone features In this paper we propose a novel structure affinity dual attention based network SADANet for effective spine segmentation Global channel attention module and spatial criss cross attention module are combined in a parallel manner to generate rich global context of spine images Meanwhile we present a structure affinity strategy to encode the structural knowledge of spine bones into the semantic representations By this means the network can capture both contextual and structural information Experiments show that our proposed algorithm achieves promising performance on spine segmentation as compared with other state of the art candidates which makes it an appealing approach for intelligent scoliosis assessment
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