基于空间自注意力机制和深度特征重建的脑MR图像分割方法

doi:10.12068/j.issn.1005-3026.2023.02.004

东北大学学报（自然科学版） ›› 2023, Vol. 44 ›› Issue (2): 177-185.DOI: 10.12068/j.issn.1005-3026.2023.02.004

基于空间自注意力机制和深度特征重建的脑MR图像分割方法

魏颖¹，林子涵¹，齐林¹，李伯群²

(1. 东北大学信息科学与工程学院，辽宁沈阳110819； 2. 辽宁科技大学电子与信息工程学院，辽宁鞍山114051)

修回日期:2021-12-10 接受日期:2021-12-10 发布日期:2023-02-27
通讯作者: 魏颖
作者简介:魏颖(1968-)，女，辽宁本溪人，东北大学教授，博士生导师；李伯群(1970-)，男，辽宁鞍山人，辽宁科技大学教授.
基金资助:
国家自然科学基金资助项目(61871106)；辽宁省重点研发计划项目(2020JH2/10100029).

Brain MR Image Segmentation Based on Spatial Self-attention Mechanism and Depth Feature Reconstruction

WEI Ying¹， LIN Zi-han¹， QI Lin¹， LI Bo-qun²

1. School of Information Science & Engineering， Northeastern University， Shenyang 110819，China; 2. School of Electronic & Information Engineering， University of Science and Technology Liaoning， Anshan 114051，China.

Revised:2021-12-10 Accepted:2021-12-10 Published:2023-02-27
Contact: LI Bo-qun
About author:-
Supported by:
-

摘要/Abstract

摘要： 准确分割核磁共振(magnetic resonance，MR)图像中的脑组织是临床诊断、手术计划和辅助治疗的关键步骤.深度学习在各种图像分割任务中表现出巨大潜力，现有模型没有一种有效方法汇总远距离像素间的关系.在网络解码阶段不能很好地融合不同层级的特征，导致无法准确定位.为克服上述问题，本文提出一种基于空间自注意力机制和深度特征重建的脑MR图像分割方法，构建了一个可以融合3维信息的2D模型，可快速准确对3D结构图像进行密集预测.在MRBrainS13数据集和IBSR数据集上进行充分地实验研究，结果表明本文方法在3D多模态和单模态脑MR图像分割方面优于目前的2D模型，运算和推理时间相比3D模型小很多，性能却十分接近.

关键词: 脑图像分割；全卷积网络；空间自注意力；通道注意力；深度特征重建

Abstract: Accurate segmentation of brain tissue in MR images is a key step in clinical diagnosis， surgical planning and adjuvant treatment. Deep-learning shows great potential in various image segmentation tasks， and existing models do not have an effective way to summarize the relationship between long-distance pixels. In the network decoding stage， the features of different levels cannot be well integrated， resulting in the inability to accurately locate. To overcome the above problems， this paper proposes a brain MR image segmentation method based on spatial self-attention mechanism and depth feature reconstruction， and constructs a 2D model that can fuse 3D information， which can quickly and accurately perform dense prediction on 3D structural images. The proposed method is fully experimented on MRBrainS13 data sets and IBSR data sets， and the results show that the model outperforms the current 2D model in 3D multimodal and unimodal brain MR image segmentation， with less computing and inference time compared to the 3D model， whereas the performance is very close.

Key words: brain image segmentation; fully convolutional network; spatial self-attention; channel attention; depth feature reconstruction

中图分类号:

TP20

魏颖，林子涵，齐林，李伯群. 基于空间自注意力机制和深度特征重建的脑MR图像分割方法[J]. 东北大学学报（自然科学版）, 2023, 44(2): 177-185.

WEI Ying， LIN Zi-han， QI Lin， LI Bo-qun. Brain MR Image Segmentation Based on Spatial Self-attention Mechanism and Depth Feature Reconstruction[J]. Journal of Northeastern University(Natural Science), 2023, 44(2): 177-185.

参考文献

[1]Nuzziello N，Ciaccia L，Liguori M.Precision medicine in neurodegenerative diseases: some promising tips coming from the microRNAs′ world［J］.Cells，2020，9(1).DOI:10.3390/cells9010075.
[2]Akkus Z，Galimzianova A，Hoogi A，et al.Deep learning for brain MRI segmentation: state of the art and future directions［J］.Journal of Digital Imaging，2017，30(4):449-459.
[3]Minaee S，Boykov Y Y，Porikli F，et al.Image segmentation using deep learning: a survey［J］.IEEE Transactions on Pattern Analysis and Machine Intelligence，2022，44(7):523-3542.
[4]Devunooru S，Alsadoon A，Chandana P W C，et al.Deep learning neural networks for medical image segmentation of brain tumours for diagnosis: a recent review and taxonomy［J］.Journal of Ambient Intelligence and Humanized Computing，2021，12(1):455-483.
[5]Moeskops P，Viergever M A，Mendrik A M，et al.Automatic segmentation of MR brain images with a convolutional neural network［J］.IEEE Transactions on Medical Imaging，2016，35(5):1252-1261.
[6]Chen H，Dou Q，Yu L，et al.VoxResNet: deep voxelwise residual networks for brain segmentation from 3D MR images［J］.NeuroImage，2018，170: 446-455.
[7]Sun K，Xiao B，Liu D，et al.Deep high-resolution representation learning for human pose estimation［C］//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Long Beach，2019:5693-5703.
[8]Gu R，Wang G，Song T，et al.CA-Net: comprehensive attention convolutional neural networks for explainable medical image segmentation［J］.IEEE Transactions on Medical Imaging，2020，40(2):699-711.
[9]Lyksborg M，Puonti O，Agn M，et al.An ensemble of 2D convolutional neural networks for tumor segmentation［C］// Scandinavian Conference on Image Analysis.New York: Springer，2015:201-211.
[10]Long J，Shelhamer E，Darrell T.Fully convolutional networks for semantic segmentation［J］.IEEE Transactions on Pattern Analysis & Machine Intelligence，2017，39(4): 640-651.
[11]Ronneberger O，Fischer P，Brox T.U-Net: convolutional networks for biomedical image segmentation［C］//International Conference on Medical Image Computing & Computer-assisted Intervention.Munich，2015.DOI:10.1007/978-3-319-24574-4_28.
[12]Dolz J，Desrosiers C，Ayed I B.3D fully convolutional networks for subcortical segmentation in MRI: a large-scale study［J］.NeuroImage，2017，170:456-470.
[13]Cao X，Lin Y.Caggnet: crossing aggregation network for medical image segmentation［C］//2020 25th International Conference on Pattern Recognition(ICPR).Milan:IEEE，2020:1744-1750.
[14]Isensee F，Jaeger P F，Kohl S A A，et al.U-Net: a self-configuring method for deep learning-based biomedical image segmentation［J］.Nature Methods，2021，18(2):203-211.
[15]Chen L C，Papandreou G，Kokkinos I，et al.DeepLab: semantic image segmentation with deep convolutional nets，atrous convolution，and fully connected CRFs［J］.IEEE Transactions on Pattern Analysis and Machine Intelligence，2018，40(4):834-848.
[16]Francesco V，Adriana R，Kyunghyun C，et al. ReSeg: a recurrent neural network-based model for semantic segmentation［C］// Proceedings of 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops.Las Vegas，2016: 426-433.
[17]Sérgio P，Pinto A，Oliveira J，et al.Automatic brain tissue segmentation in MR images using random forests and conditional random fields［J］.Journal of Neuroscience Methods，2016，270:111-123.
[18]Rajchl M，Baxter J S，Mcleod A J，et al.Hierarchical max-flow segmentation framework for multi-atlas segmentation with Kohonen self-organizing map based Gaussian mixture modeling［J］.Medical Image Analysis，2016:27:45-56.
[19]Andermatt S，Pezold S，Cattin P.Multi-dimensional gated recurrent units for the segmentation of biomedical 3D-data［M］.Cham: Springer International Publishing，2016.https://doi.org/10.1007/978-3-319-46976-8_15.
[20]Stollenga M F，Byeon W，Liwicki M，et al.Parallel multi-dimensional LSTM with application to fast biomedical volumetric image segmentation［C］//29th Annual Conference on Neural Information Processing Systems(NIPS).Montreal，2015:464-450.
[21]Valverde S，Oliver A，Cabezas M，et al.Comparison of 10 brain tissue segmentation methods using revisited IBSR annotations［J］.Journal of Magnetic Resonance Imaging，2015，41(1):93-101.

基于空间自注意力机制和深度特征重建的脑MR图像分割方法

Brain MR Image Segmentation Based on Spatial Self-attention Mechanism and Depth Feature Reconstruction

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	耿蓉，吴亚倩，肖倩倩，徐赛. 基于改进GRU算法的天基信息网资源预测研究[J]. 东北大学学报（自然科学版）, 2023, 44(3): 305-314.
[2]	罗忠，吴东泽，李雷，葛长闯. 转子系统动力学仿真平台设计与实验验证[J]. 东北大学学报（自然科学版）, 2023, 44(3): 375-381.
[3]	侯振隆，郑玉君，巩恩普，程浩. 基于深度加权的重力梯度数据联合相关成像反演[J]. 东北大学学报:自然科学版, 2020, 41(11): 1628-1632.
[4]	吴超，李元辉，徐帅，戴星航. 地下工程岩体节理迹长统计下限值的确定方法[J]. 东北大学学报:自然科学版, 2020, 41(8): 1167-1173.
[5]	温守钦，唐铁乔，谢伟，付建飞. 氧、硫逸度对岫岩红旗铅锌矿床矿物组合共生分异的制约[J]. 东北大学学报:自然科学版, 2020, 41(7): 999-1007.
[6]	齐锡晶，唐梁，康伟鑫，秦娇娇. 高速公路桥梁桥面板养护方案多目标决策方法[J]. 东北大学学报:自然科学版, 2020, 41(7): 1033-1040.
[7]	蔡志辉，张德良，周彦君，文光奇. 应变速率对Fe-11Mn-2Al-0.2C中锰钢变形行为的影响[J]. 东北大学学报:自然科学版, 2020, 41(7): 1041-1047.
[8]	马树军，王霄霄. 吸附微粒对微悬空桥传感器的影响[J]. 东北大学学报:自然科学版, 2020, 41(1): 108-112.
[9]	马树军，玄航，孙嘉蔚，杨磊. 在非黏性流体域中微悬臂梁振动的建模与仿真[J]. 东北大学学报:自然科学版, 2018, 39(9): 1272-1276.
[10]	马树军，修强. 考虑表面应力的微悬臂梁质量传感器的建模与仿真[J]. 东北大学学报:自然科学版, 2018, 39(2): 237-241.
[11]	陈大力，沈岩涛，谢槟竹，马颖异. 基于余弦相似度模型的最佳教练遴选算法[J]. 东北大学学报:自然科学版, 2014, 35(12): 1697-1700.
[12]	潘峰，刘禄，薛定宇. 最优分数阶PIλDμ网络时延控制器[J]. 东北大学学报:自然科学版, 2014, 35(10): 1382-1385.
[13]	刘禄，潘峰，薛定宇. 一种分数阶卡尔曼滤波器[J]. 东北大学学报:自然科学版, 2014, 35(8): 1069-1072.
[14]	刘金海，冯健，马大中. 流体管道压力信号的高精度实时滤波方法[J]. 东北大学学报:自然科学版, 2013, 34(1): 9-12.
[15]	孙艳蕊. 带时间和成本约束的随机流网络可靠度的计算[J]. 东北大学学报（自然科学版）, 2013, 34(11): 1537-1541.