Abstract
Multi-class segmentation of vertebrae and intervertebral discs is of paramount importance for accurate diagnosis and treatment of various spinal diseases. However, achieving precise segmentation remains a challenging task. In this study, we propose a modified BiSeNet specifically designed for spine segmentation, aiming to address the limitations and improve the segmentation performance. Our motivation behind modifying BiSeNet lies in fully leveraging the strengths of both the context and spatial paths in feature extraction based on the BiSeNet. To enhance segmentation effectiveness, we fuse the two paths, which consist of a context extractor and a spatial extractor, using the feature fusion module (FFM) in BiSeNet. We utilize a U-shaped architecture for the context path, incorporating attention refinement modules (ARM) to leverage features from all encoder layers. Additionally, a specific residual structure is introduced to enhance the context extractor’s effectiveness. For the spatial path, we introduce a novel multi-scale convolution attention module based on the SegNext structure. These main contributions in our framework improve the segmentation effectiveness by capturing long-range relationships among vertebrae and intervertebral discs while considering inter-class similarity and intra-class variation. Experimental results on an MRI dataset comprising 172 subjects demonstrate impressive performance, with a mean Dice similarity coefficient of 82.365% across all spinal structures. These results indicate the considerable potential of our proposed method in aiding the diagnosis and treatment of spinal diseases.
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Acknowledgment
This work was supported in part by the Project of Shenzhen Science and Technology Innovation Committee, China, under Grant KCXFZ20201221173202007; in part by the Key Scientific Research Platforms and Projects of Guangdong Regular Institutions of Higher Education, China, under Grant 2022KCXTD033; in part by the Scientific Research Capacity Improvement Project of Key Developing Disciplines in Guangdong Province, China, under Grant 2021ZDJS084; in part by the Guangdong Natural Science Foundation, China, under Grant 2023A1515012103; and in part by the Fundamental Public Welfare Research Project of Zhejiang Province, China, under Grant LGG21E050022.
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Deng, Y. et al. (2023). A Modified BiSeNet for Spinal Segmentation. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14269. Springer, Singapore. https://doi.org/10.1007/978-981-99-6489-5_11
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DOI: https://doi.org/10.1007/978-981-99-6489-5_11
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