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An Automated Multi-scale Feature Fusion Network for Spine Fracture Segmentation Using Computed Tomography Images

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Abstract

Spine fractures represent a critical health concern with far-reaching implications for patient care and clinical decision-making. Accurate segmentation of spine fractures from medical images is a crucial task due to its location, shape, type, and severity. Addressing these challenges often requires the use of advanced machine learning and deep learning techniques. In this research, a novel multi-scale feature fusion deep learning model is proposed for the automated spine fracture segmentation using Computed Tomography (CT) to these challenges. The proposed model consists of six modules; Feature Fusion Module (FFM), Squeeze and Excitation (SEM), Atrous Spatial Pyramid Pooling (ASPP), Residual Convolution Block Attention Module (RCBAM), Residual Border Refinement Attention Block (RBRAB), and Local Position Residual Attention Block (LPRAB). These modules are used to apply multi-scale feature fusion, spatial feature extraction, channel-wise feature improvement, segmentation border results border refinement, and positional focus on the region of interest. After that, a decoder network is used to predict the fractured spine. The experimental results show that the proposed approach achieves better accuracy results in solving the above challenges and also performs well compared to the existing segmentation methods.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Central South University, Changsha, 410083, Hunan, China

    Muhammad Usman Saeed, Wang Bin & Jinfang Sheng

  2. Computer and Network Engineering Department, College of Computer and Information Systems, Umm Al-Qura University, Makkah, 24382, Saudi Arabia

    Hussain Mobarak Albarakati

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  1. Muhammad Usman Saeed
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  2. Wang Bin
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  3. Jinfang Sheng
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  4. Hussain Mobarak Albarakati
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Contributions

Muhammad Usman Saeed and Wang Bin conceptualized the study, with contributions from Jinfang Sheng. Methodology, formal analysis, data curation, and writing original draft were prepared by Muhammad Usman Saeed. Methodology development, validation, supervision, and review original draft was done by Wang Bin. Muhammad Usman Saeed and Wang Bin validated the methodology. Formal analysis, investigation efforts, data curation, and supervision were conducted by Jinfang Sheng. Data curation, validation, and formal analysis were conducted by Hussain Mobarak Albarakati.

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Correspondence to Wang Bin.

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Saeed, M.U., Bin, W., Sheng, J. et al. An Automated Multi-scale Feature Fusion Network for Spine Fracture Segmentation Using Computed Tomography Images. J Digit Imaging. Inform. med. (2024). https://doi.org/10.1007/s10278-024-01091-0

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  • DOI: https://doi.org/10.1007/s10278-024-01091-0

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