Medical Image Segmentation Model Based on Involution U-Net

doi:10.19678/j.issn.1000-3428.0062023

Abstract

Abstract: The main objects of image segmentation technology are natural images and medical images.Compared with that of natural images, semantic segmentation of medical images usually requires high accuracy for the subsequent steps of clinical analysis, diagnosis, and treatment planning.At present, the depth neural network model used to semantically segment medical images only considers the translation invariance of position, which features an insufficiently large local receptive field, leaving no way to express long-range dependence.Therefore, medical image segmentation model is proposed in this paper.Based on the Involution U-Net network, the involution operation replaces the traditional convolution operation, and the involution structure is adopted as the basic network structure to tailor the model's learning ability to the local features of medical images.To improve the model's learning ability, Besides, the attention mechanism module is introduced into the bottleneck layer to learn the long-range dependency of images.Experimental results on the lung CT dataset show that the model's Dice coefficient is 0.998, which is approximately 5% higher than that of the current segmentation model based on a convolutional neural network.In addition, this model greatly cuts the Hausdorff distance, apart from achieving higher segmentation accuracy and better robustness.

Key words: image segmentation, involution network, image processing, attention mechanism, U-Net network

摘要： 图像分割技术的主要对象为自然图像和医学图像，相对于自然图像而言，医学图像的语义分割通常需要较高的精度以进行下一步的临床分析、诊断和规划治疗。目前用于医学图像语义分割的深度神经网络模型由于仅考虑位置的平移不变性，存在局部感受野较小、无法表达长范围依赖关系的问题。设计一种面向医学图像的分割模型，基于内卷U-Net网络，使用内卷操作代替传统的卷积操作，并将内卷结构作为基本的网络结构，提升模型对医学图像局部特征的学习能力。在模型的瓶颈层引入注意力机制模块来学习图像长范围的依赖关系，以提高医学图像语义分割的精度。在肺部CT数据集上的实验结果表明，该模型的Dice系数为0.998，较基于卷积神经网络的分割模型约提高5%，并且大幅缩短Hausdorff距离，具有更高的分割准确度以及较好的稳健性。

关键词: 图像分割, 内卷网络, 图像处理, 注意力机制, U-Net网络

CLC Number:

TP391

LIN Zhijie, ZHENG Qiulan, LIANG Yong, XING Wei. Medical Image Segmentation Model Based on Involution U-Net[J]. Computer Engineering, 2022, 48(8): 180-186.

林志洁, 郑秋岚, 梁涌, 邢卫. 基于内卷U-Net的医学图像分割模型[J]. 计算机工程, 2022, 48(8): 180-186.

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