MCA-Net：多尺度综合注意力CNN在医学图像分割中的应用

丁才富; 杨晨; 纪秋浪; 王阳; 张兵

doi:10.19304/J.ISSN1000-7180.2021.0950

Chinese

您当前的位置：

首页 >

文章列表页 >

MCA-Net：多尺度综合注意力CNN在医学图像分割中的应用

计算机工程与应用 | 更新时间：2026-09-04

- MCA-Net：多尺度综合注意力CNN在医学图像分割中的应用
- MCA-Net: Multi-scale comprehensive attention applicaton of CNN in medical image segmentation
- 微电子学与计算机 2022年39卷第3期页码：71-77
- 作者机构：
  
  贵州大学大数据与信息工程学院,贵州,贵阳,550025
- 作者简介：
- 基金信息：
  
  国家自然基金 62065003半导体功率器件可靠性教育部工程研究中心开放基金黔科合平台人才20176103
- DOI：10.19304/J.ISSN1000-7180.2021.0950
  中图分类号：
- 纸质出版：2022
- 稿件说明：
移动端阅览
丁才富, 杨晨, 纪秋浪, 王阳, 张兵. MCA-Net：多尺度综合注意力CNN在医学图像分割中的应用[J]. 微电子学与计算机, 2022, 39(3): 71-77.

DING Caifu, YANG Chen, JI Qiulang, et al. MCA-Net: Multi-scale comprehensive attention applicaton of CNN in medical image segmentation[J]. 2022, 39(3): 71-77.
丁才富, 杨晨, 纪秋浪, 王阳, 张兵. MCA-Net：多尺度综合注意力CNN在医学图像分割中的应用[J]. 微电子学与计算机, 2022, 39(3): 71-77. DOI： 10.19304/J.ISSN1000-7180.2021.0950.

DING Caifu, YANG Chen, JI Qiulang, et al. MCA-Net: Multi-scale comprehensive attention applicaton of CNN in medical image segmentation[J]. 2022, 39(3): 71-77. DOI： 10.19304/J.ISSN1000-7180.2021.0950.

摘要

医学图像自动分割技术具有辅助临床医学诊断的功能.为改善CNN模型在医学图像分割中存在感受野小及细节特征不敏感等问题，基于多尺度策略以及注意力机制，提出一种多尺度综合注意力的U形网络架构，以提升医学图像分割质量.首先，提出一个新的双路径因式分解多尺度融合块，以扩展图像特征的感受野，进一步提取图像特征的细节信息.其次，在架构中融入通道和空间融合自注意力块，利用注意力机制的特性，抑制不相关的部分或背景以突显深层特征的空间信息.最后，引入多尺度注意力块.该模块通过融合多个尺度的特征信息，以突出不同尺度中最显著的特征图来适应当前分割对象的大小.为验证模型的可靠性，将所提出的网络模型应用于肺部、细胞轮廓及肝脏等医学图像分割任务.实验结果表明，所提方法在准确率、Dice系数、AUC及灵敏度等评估指标上均优于目前用于医学图像分割的主流方法.

Abstract

The automatic segmentation technology of medical images has the function of assisting clinical medical diagnosis. In order to improve the CNN model in medical image segmentation

such as small receptive fields and insensitive detail features

a multi-scale integrated attention U-shaped network architecture is proposed to improve medical image segmentation quality

which is based on multi-scale strategies and attention mechanisms. First

a new dual-path factorization multi-scale fusion block is proposed to expand the receptive field of image features and further extract the detailed information of image features. Secondly

integrate channel and space fusion self-attention blocks into the architecture

and use the characteristics of the attention mechanism to suppress irrelevant parts or background to highlight the spatial information of deep features. Finally

a multi-scale attention block is introduced. The module fuses feature information of multiple scales to highlight the most prominent feature maps in different scales to adapt to the size of the current segmentation object. In order to verify the reliability of the model

the proposed network model is applied to medical image segmentation tasks such as lungs

cell contours

and liver. Experimental results show that the proposed method is superior to the current mainstream methods for medical image segmentation in terms of accuracy

Dice coefficient

AUC

and sensitivity. It is the mainstream method currently used for medical image segmentation.

关键词

Keywords

references

ANWAR S M, MAJID M, QAYYUM A, et al. Medical image analysis using convolutional neural networks: a review[J]. Journal of Medical Systems, 2018, 42(11): 226. DOI: 10.1007/s10916-018-1088-1.

HESAMIAN M H, JIA W J, HE X J, et al. Deep learning techniques for medical image segmentation: achievements and challenges[J]. Journal of Digital Imaging, 2019, 32(4): 582-596. DOI: 10.1007/s10278-019-00227-x.

BILIC P, CHRIST P F, VORONTSOV E, et al. The liver tumor segmentation benchmark (LiTS)[C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019: 1-43.

FU J, LIU J, TIAN H J, et al. Dual attention network for scene segmentation[C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 3141-3149. DOI: 10.1109/CVPR.2019.00326.

LIU L L, CHENG J H, QUAN Q, et al. A survey on U-shaped networks in medical image segmentations[J]. Neurocomputing, 2020, 409: 244-258. DOI: 10.1016/j.neucom.2020.05.070.

MOU L, ZHAO Y T, CHEN L, et al. CS-Net: channel and spatial attention network for curvilinear structure segmentation[C]//22nd International Conference on Medical Image Computing and Computer Assisted Intervention. Shenzhen, China: Springer, 2019: 721-730. DOI: 10.1007/978-3-030-32239-7_80.

CARDONA A, SAALFELD S, PREIBISCH S, et al. An integrated micro- and macroarchitectural analysis of the drosophila brain by computer-assisted serial section electron microscopy[J]. PLoS Biology, 2010, 8(10): e1000502. DOI: 10.1371/journal.pbio.1000502.

GU Z W, CHENG K, FU H Z, et al. CE-Net: context encoder network for 2D medical image segmentation[J]. IEEE Transactions on Medical Imaging, 2019, 38(10): 2281-2292. DOI: 10.1109/TMI.2019.2903562.

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. DOI: 10.1109/TPAMI.2017.2699184.

OKTAY O, SCHLEMPER J, LE FOLGOC L, et al. Attention U-Net: learning where to look for the pancreas[C]//1st Conference on Medical Imaging with Deep Learning. Amsterdam, [s. l. ]. 2018: 1-10.

RONNEBERGER O, FISCHER P, BROX T. U-Net: convolutional networks for biomedical image segmentation[C]//18th International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich: Springer, 2015: 234-241. DOI: 10.1007/978-3-319-24574-4_28.

ZHOU Z W, RAHMAN SIDDIQUEE M, TAJBAKHSH N, et al. UNet++: a nested u-net architecture for medical image segmentation[C]//4th International Workshop on Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Granada: Springer, 2018: 3-11. DOI: 10.1007/978-3-030-00889-5_1.

HOERL A E, KENNARD R W. Ridge regression: biased estimation for nonorthogonal problems[J]. Technometrics, 1970, 12(1): 55-67. DOI: 10.2307/1271436.

CHEN H, DOU Q, YU L Q, et al. VoxResNet: deep voxelwise residual networks for brain segmentation from 3D MR images[J]. NeuroImage, 2018(170): 446-455. DOI: 10.1016/j.neuroimage.2017.04.041.

ARMATO Ⅲ S G, MCLENNAN G, BIDAUT L, et al. The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans[J]. Medical Physics, 2011, 38(2): 915-931. DOI: 10.1118/1.3528204.

MILLETARI F, NAVAB N, AHMADI S A. V-Net: fully convolutional neural networks for volumetric medical image segmentation[C]//2016 Fourth International Conference on 3D Vision. Stanford: IEEE, 2016: 565-571. DOI: 10.1109/3DV.2016.79.

LÊ M, UNKELBACH J, AYACHE N, et al. GPSSI: gaussian process for sampling segmentations of images[C]//18th International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich: Springer, 2015: 38-46. DOI: 10.1007/978-3-319-24574-4_5.

GU R, WANG G T, SONG T, et al. CA-Net: comprehensive attention convolutional neural networks for explainable medical image segmentation[J]. IEEE Transactions on Medical Imaging, 2021, 40(2): 699-711. DOI: 10.1109/TMI.2020.3035253.

SINGH V K, ABDEL-NASSER M, RASHWAN H A, et al. FCA-Net: adversarial learning for skin lesion segmentation based on multi-scale features and factorized channel attention[J]. IEEE Access, 2019(7): 130552-130565. DOI: 10.1109/ACCESS.2019.2940418.

纪玲玉, 高永彬, 卫子然. MFA-Net: 基于多视野上下文关注的视网膜血管分割[J]. 微电子学与计算机, 2021, 38(3): 14-20. DOI: 10.19304/j.cnki.issn1000-7180.2021.03.003. JI L Y, GAO Y B, WEI Z R. MFA-Net: retinal vessel segmentation based on multi-view context attention[J]. Microelectronics % Computer, 2021, 38(3): 14-20. DOI: 10.19304/j.cnki.issn1000-7180.2021.03.003.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

AlN/GaN混合缓冲层双异质结p-GaN HEMT直流特性仿真

基于改进RT-DETR的航拍小目标检测算法

一种用于极低压boost系统的高效率电荷泵

一种基于特征上下文理解与切片分析的肺结核检测算法

基于区块链的AI生成内容溯源框架设计研究