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Cervical Spondylotic Myelopathy Segmentation Using Shape-Aware U-net

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Neural Computing for Advanced Applications (NCAA 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1449))

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Abstract

Cervical Spondylotic Myelopathy (CSM) is serious cervical spondylosis that can lead to severe disability. To help physicians make diagnoses quickly and efficiently, automatic segmentation methods are urgently needed in clinical practice. Nevertheless, there are great challenges with this task, such as ambiguity of structure boundary and uncertainty of the segmented region. Although some deep learning methods have performed well in medical segmentation tasks, they are not good at processing complex medical images. To solve those problems in automatic medical segmentation, this paper proposes a novel shape-aware segmentation framework for the cervical spondylotic myelopathy segmentation from diffusion tensor imaging (DTI). Specifically, a new shape-aware strategy was adopted that enables backbone networks to simultaneously aggregate both global and local context and efficiently capture long-range dependencies. Extending pyramid pooling with a shape-aware strategy, the shape-aware pyramid pooling module(SAAP) was adopted to integrate multi-scale information and compensate for spatial information loss. This module expands the field of perception and reduces interference in non-lesioned areas. The effectiveness of shape-aware U-net (SAU-net) was evaluated on the cervical spondylotic myelopathy dataset, which consists of 116 patients who underwent surgical decompression and DTI evaluation. The experiment proves that our method can effectively segment CSM lesions.

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Acknowledgement

This work is supported by National Natural Science Foundations of China under Grant No. 61872351, International Science and Technology Cooperation Projects of Guangdong under Grant 2019A050510030, Guangdong Science Fund for Distinguished Young Scholars under Grant 2021B1515020019, Shenzhen Key Basic Research Project under Grant No. JCYJ20200109115641762 and Shenzhen Science Fund for Excellent Young Scholars under Grant No. RCYX2020071411464121.

Author information

Authors and Affiliations

  1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China

    Zhuo Chen, Shuqiang Wang & Yanyan Shen

  2. Neural Engineering Laboratory, The University of Hong Kong, Hong Kong, China

    Yong Hu

  3. United Kingdom University of Leicester, Leicester, UK

    Huiyu Zhou

  4. The University of Hong Kong-Shenzhen Hosptial, Shenzhen, China

    Xiang Li

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  1. Zhuo Chen
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  3. Yong Hu
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  6. Xiang Li
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Corresponding author

Correspondence to Shuqiang Wang .

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Editors and Affiliations

  1. Harbin Institute of Technology, Shenzhen, China

    Haijun Zhang

  2. Nanfang College of Sun Yat-sen University, Guangzhou, China

    Zhi Yang

  3. Hefei University of Technology, Hefei, China

    Zhao Zhang

  4. Chongqing University, Chongqing, China

    Zhou Wu

  5. South China Normal University, Guangzhou, China

    Tianyong Hao

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Chen, Z., Wang, S., Hu, Y., Zhou, H., Shen, Y., Li, X. (2021). Cervical Spondylotic Myelopathy Segmentation Using Shape-Aware U-net. In: Zhang, H., Yang, Z., Zhang, Z., Wu, Z., Hao, T. (eds) Neural Computing for Advanced Applications. NCAA 2021. Communications in Computer and Information Science, vol 1449. Springer, Singapore. https://doi.org/10.1007/978-981-16-5188-5_48

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  • DOI: https://doi.org/10.1007/978-981-16-5188-5_48

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