基于BM-TransUNet的咽后壁识别分割
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广西科技基地和人才专项(AD22080004)


Posterior Pharyngeal Wall Recognition and Segmentation Based on BM-TransUNet
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    摘要:

    图像分割经历了从基于传统的阈值分割等方法逐步发展到基于卷积神经网络的方法. 传统的卷积神经网络在分割领域中表现突出, 但训练速度慢、分割精度不够高等局限性也逐渐显现. 为了克服这些局限性, 本文在TransUNet网络的基础上进行改进, 提出了基于BM-TransUNet网络的图像分割识别方法, 在TransUNet网络的在第1层之后加上深度可分离卷积模块, 并在编码器下采样的卷积层后引入注意力机制模块, 让算法更好地探索分割对象特征, 同时在编码器与解码器之间引入多尺度特征融合模块FPN. 本文基于自制的咽后壁数据集, 用于图像分割训练, 并将训练后的BM-TransUNet网络与多种传统分割网络的效果进行对比. 实验证明, 相比其他传统的深度学习模型, BM-TransUNet网络的识别方法具有较高的分类准确性和泛化能力, 精确度PrecisionDice系数分别达到了93.61%和90.76%, 显示出较好的计算效率, 能有效地应用于分割任务.

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    Image segmentation has gradually developed from traditional threshold-based methods to convolutional neural network (CNN)-based methods. Traditional CNNs are outstanding in the field of segmentation, but the limitations of slow training speed and low segmentation accuracy are gradually emerging. To overcome these limitations, this study proposes an image segmentation recognition method based on the BM-TransUNet network, which is an improvement. A depth-separable convolution module is added to the first layer of the TransUNet network, and an attention mechanism module is introduced to the convolution layer of the encoder under-sampling so that the algorithm can better explore the features of the segmented objects. At the same time, a multi-scale feature fusion module, the feature pyramid network (FPN), is introduced between the decoder and encoder. In this study, a self-made posterior pharyngeal wall dataset is used for image segmentation training, and the effects of the trained BM-TransUNet network are compared with various traditional segmentation networks. Experimental results show that, compared to other traditional deep learning models, the identification method of the BM-TransUNet network exhibits higher classification accuracy and generalization ability, with Precision and Dice coefficient of 93.61% and 90.76%, respectively, showing better computational efficiency and effective in segmentation tasks.

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王世刚,孙静雯.基于BM-TransUNet的咽后壁识别分割.计算机系统应用,,():1-9

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  • 收稿日期:2023-12-09
  • 最后修改日期:2024-01-09
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  • 在线发布日期: 2024-05-31
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