[1]黄 莉,何美玲*.基于 U-Net 改进模型的多模态脑肿瘤分割方法[J].计算机技术与发展,2022,32(11):58-63.[doi:10. 3969 / j. issn. 1673-629X. 2022. 11. 009]
　HUANG Li,HE Mei-ling*.Multi-model Brain Tumor Segmentation Method Based on Improved U-Net Model[J].,2022,32(11):58-63.[doi:10. 3969 / j. issn. 1673-629X. 2022. 11. 009]
点击复制

基于 U-Net 改进模型的多模态脑肿瘤分割方法()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年11期
页码:
58-63
栏目:
媒体计算
出版日期:
2022-11-10

文章信息/Info

Title:
Multi-model Brain Tumor Segmentation Method Based on Improved U-Net Model
文章编号:
1673-629X(2022)11-0058-06
作者:
黄 莉1; 2 ; 何美玲1; 2*
1. 武汉科技大学 计算机科学与技术学院,湖北 武汉 430065;
2. 湖北省智能信息处理与实时工业系统重点实验室,湖北 武汉 430065
Author(s):
HUANG Li1; 2 ; HE Mei-ling1; 2*
1. School of Computer Science and Technology,Wuhan University of Science and Technology,Wuhan 430065,China;
2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial,Wuhan 430065,China
关键词:
脑肿瘤; U-Net; 卷积神经网络; 图像分割; 多尺度策略; 自注意力机制
Keywords:
brain tumor; U-Net; convolutional neural network; image segmentation; multi-scale strategy; self-attention mechanism
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 11. 009
摘要:
诊断脑肿瘤时,如果能从多种模态的核磁共振成像( MRI) 图像中精准分割出脑肿瘤区域,将有助于医生快速和准确的诊断。 针对分割脑肿瘤时出现的边界分割不精准问题,该文提出了一种基于 U-Net 改进模型的脑肿瘤分割方法。 该方法将 U-Net 每级编码器的特征图保留,来捕获分割目标的边界细节信息,进而对保留的特征图采用自注意力模块计算通道级别注意力,加强分割目标的边界空间信息提取,最后使用尺度融合模块统一特征图的尺度和通道数,来融合分割目标的边界信息,作为解码器的输入,从而提高分割性能。 该方法在 BRATS2017 数据集上进行训练和测试,在 Dice、SE、SP三个评估指标的参考下,通过消融实验证明了融合多尺度模块和自注意力机制的有效性,并与 U-Net、ResUNet、SGNet、RelayNet 四种网络模型进行对比实验,表明由于融合了分割目标边界的细节和空间信息,该模型得到的分割区域更加接近真实脑肿瘤区域。
Abstract:
When diagnosing brain tumors,if the brain tumor area can be accurately segmented from multiple modal MRI images,it willhelp doctors make a quick and accurate diagnosis. Aiming at the problem of inaccurate boundary segmentation when segmenting braintumors,we propose a brain tumor segmentation method based on U-Net model. This method retains the feature map of each level of U-Net encoder to capture the boundary detail information of the segmentation target,uses the self-attention module to calculate the channel-level attention of the retained feature map,and strengthens the boundary space information extraction of the segmentation target. Finally,the scale fusion module is used to unify the scale and the number of channels of the feature map to fuse the boundary information of thesegmentation target as the input of the decoder,thereby improving the segmentation performance. The proposed method is trained and tested on the BRATS2017 data set. Under the reference of the three evaluation indicators of Dice,SE and SP,the effectiveness of thefusion of multi-scale modules and the self-attention mechanism is proved through ablation experiments. Compared with the four networkmodels of U-Net,ResUNet,SGNet and RelayNet,the segmentation area obtained by the model is closer to the real brain tumor area,dueto the integration of the details and spatial information of the segmentation target boundary.

相似文献/References:

[1]刘　鑫,赵慧杰,武苏雯.基于Ｕ－Ｎｅｔ的岩层图像裂缝检测算法应用[J].计算机技术与发展,2021,31(05):73.[doi:10. 3969 / j. issn. 1673-629X. 2021. 05. 013]
　,,et al.ＡｐｐｌｉｃａｔｉｏｎｏｆＵ－ＮｅｔＢａｓｅｄＲｏｃｋＦｏｒｍａｔｉｏｎＩｍａｇｅＣｒａｃｋＤｅｔｅｃｔｉｏｎＡｌｇｏｒｉｔｈｍ[J].,2021,31(11):73.[doi:10. 3969 / j. issn. 1673-629X. 2021. 05. 013]
[2]李博文,唐贵进,崔子冠.基于Ｒｅｔｉｎｅｘ模型的弱光照图像增强算法[J].计算机技术与发展,2021,31(05):79.[doi:10. 3969 / j. issn. 1673-629X. 2021. 05. 014]
　,ＡＷｅａｋｌｙＩｌｌｕｍｉｎａｔｅｄＩｍａｇｅＥｎｈａｎｃｅｍｅｎｔＡｌｇｏｒｉｔｈｍ ＢａｓｅｄｏｎＲｅｔｉｎｅｘＭｏｄｅｌ[J].,2021,31(11):79.[doi:10. 3969 / j. issn. 1673-629X. 2021. 05. 014]
[3]史健婷,崔闫靖,常 亮.基于优化 U-Net 网络的乳腺肿瘤区域分割方法[J].计算机技术与发展,2021,31(08):156.[doi:10. 3969 / j. issn. 1673-629X. 2021. 08. 027]
　SHI Jian-ting,CUI Yan-jing,CHANG Liang.A Segmentation Method of Breast Tumor Region Based onOptimized U-Net[J].,2021,31(11):156.[doi:10. 3969 / j. issn. 1673-629X. 2021. 08. 027]
[4]龚 安,赵 莉,姚鑫杰.基于改进的 U-Net 网络模型的气胸分割算法[J].计算机技术与发展,2021,31(10):173.[doi:10. 3969 / j. issn. 1673-629X. 2021. 10. 029]
　GONG An,ZHAO Li,YAO Xin-jie.Pneumothorax Segmentation Algorithm Based on Improved U-Net Network Model[J].,2021,31(11):173.[doi:10. 3969 / j. issn. 1673-629X. 2021. 10. 029]
[5]喻鲁立,陈 黎.基于 U-Net 的多尺度视网膜血管分割方法[J].计算机技术与发展,2022,32(04):140.[doi:10. 3969 / j. issn. 1673-629X. 2022. 04. 024]
　YU Lu-li,CHEN Li.Multi-scale Segmentation of Retinal Vessels Based on U-Net[J].,2022,32(11):140.[doi:10. 3969 / j. issn. 1673-629X. 2022. 04. 024]
[6]季倓正,邵允学,吕摇 刚.基于深度学习的钢坯入炉前定位技术[J].计算机技术与发展,2022,32(S2):36.[doi:10. 3969 / j. issn. 1673-629X. 2022. S2. 006]
　JI Tan-zheng,SHAO Yun-xue,LYU Gang.Positioning Technology of Billet before Furnace Based on Deep Learning[J].,2022,32(11):36.[doi:10. 3969 / j. issn. 1673-629X. 2022. S2. 006]
[7]罗 杨,万黎明,李 理,等.基于改进 U-Net 网络的半监督裂缝分割方法[J].计算机技术与发展,2022,32(12):179.[doi:10. 3969 / j. issn. 1673-629X. 2022. 12. 027]
　LUO Yang,WAN Li-ming,LI Li,et al.A Semi-supervised Crack Segmentation Method Based on Improved U-Net Network[J].,2022,32(11):179.[doi:10. 3969 / j. issn. 1673-629X. 2022. 12. 027]
[8]李秀华,朱水成.一种基于改进 U-Net 的肝脏肿瘤分割方[J].计算机技术与发展,2023,33(02):71.[doi:10. 3969 / j. issn. 1673-629X. 2023. 02. 011]
　LI Xiu-hua,ZHU Shui-cheng.A Liver Tumor Segmentation Method Based on Improved U-Net[J].,2023,33(11):71.[doi:10. 3969 / j. issn. 1673-629X. 2023. 02. 011]
[9]王宇昕,付晓薇,赵思宇,等.基于多层融合注意力的乳腺肿瘤图像分割方法[J].计算机技术与发展,2023,33(07):139.[doi:10. 3969 / j. issn. 1673-629X. 2023. 07. 021]
　WANG Yu-xin,FU Xiao-wei,ZHAO Si-yu,et al.Breast Tumor Images Segmentation Method Based on Multi-layer Fusion Attention[J].,2023,33(11):139.[doi:10. 3969 / j. issn. 1673-629X. 2023. 07. 021]
[10]谢宏彪,刘志勤,王庆凤,等.基于半监督的 3D 肝脏 CT 自动分割方法研究[J].计算机技术与发展,2023,33(09):149.[doi:10. 3969 / j. issn. 1673-629X. 2023. 09. 022]
　XIE Hong-biao,LIU Zhi-qin,WANG Qing-feng,et al.Automated Segmentation of 3D Liver CT Images Using Semi-supervision[J].,2023,33(11):149.[doi:10. 3969 / j. issn. 1673-629X. 2023. 09. 022]

更新日期/Last Update: 2022-11-10