An Efficient Spine Segmentation Method

Yuhang Meng, Longfei Zhou, Tianrun Xu, Junrui Wan, Xinyu Zhang, Zhong Wang
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Abstract

The spine is the most complex load-bearing structure in the human body, and herniated discs, spinal stenosis, and degenerative discs are common spinal disorders. MRI is an effective imaging method in medicine, but the identification and quantitative analysis of lesions require physician judgment, which is not only a huge workload but also carries the subjective judgment of physicians, and such drawbacks can be solved by using image segmentation technology. In this paper, we propose an efficient spine segmentation method consisting of selective preprocessing and post-processing and an improved UNET network structure. In the selective pre-post processing, meaningful parts of the MRI are selected for random input, and the selected parts are effectively restored back to the original size of the segmented image. In the improved UNET network, differing from the traditional UNET structure, the perceptual field of the image input is increased by using inflated convolution, and the attention mechanism is added in the up-sampling and down-sampling end parts for better filtering of features. The experimental results show that our method outperforms the traditional method by substantially reducing the training elapsed time and performing well in terms of the accuracy of the model.
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一种高效的脊柱分割方法
脊柱是人体最复杂的承重结构,椎间盘突出、椎管狭窄、椎间盘退变是常见的脊柱疾病。MRI是医学上一种有效的成像方法,但病变的识别和定量分析需要医生的判断,不仅工作量巨大,而且还带有医生的主观判断,利用图像分割技术可以解决这些缺点。在本文中,我们提出了一种高效的脊柱分割方法,包括选择性的预处理和后处理以及改进的UNET网络结构。在选择性的前后处理中,选取MRI图像中有意义的部分进行随机输入,所选取的部分有效地恢复到分割后图像的原始大小。在改进的UNET网络中,与传统的UNET结构不同,通过膨胀卷积增加了图像输入的感知场,并在上采样和下采样端加入了注意机制,以更好地过滤特征。实验结果表明,我们的方法大大减少了训练时间,在模型的准确性方面表现良好,优于传统方法。
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