摘要
针对医学图像分割领域长期存在的多目标尺度变化大和边界模糊以致分割困难的问题,提出了一种新型的基于混洗特征编码和门控解码的双分支混合网络框架用于多器官精准分割.为了充分利用卷积神经网络(Convolutional Neural Network,CNN)在局部信息提取方面和Transformer在长程依赖关系建模方面的优势,采用U-Net和SwinUnet构建双分支网络.该方法的创新之处在于对不同网络分支的多个阶段学习到的高维特征进行混洗操作,通过双支路通道交叉融合的方式实现局部信息与全局信息的高效融合,加强了双分支网络在不同阶段间的信息交互,从而解决了图像目标轮廓模糊引起的分割精度受限的问题.此外,为了解决多器官尺度变化大的问题,进一步提出了一种全新的基于多尺度特征图的门控解码器(Gated Decoder based on Multi-scale Feature,GDMF).该解码器能够学习网络不同阶段的多尺度高维特征并进行自适应特征增强,采用注意力机制和特征映射来辅助获取精准目标信息.实验结果表明,与现有主流医学图像分割方法相比,所提方法在ACDC(Automated Cardiac Diagnosis Challenge)和FLARE21(Fast and Low GPU memory Abdominal oRgan sEgmentation challenge 2021)数据集上均表现出更优的性能,有效解决了医学图像中多目标尺度变化大和边界模糊问题.
To solve the long-standing problems of the great scale variation in target sizes and blurred boundaries that make segmentation difficult in medical image segmentation,we propose a novel dual-branch hybrid network framework based on feature encoding and gated decoder based on multi-scale feature for accurate multi-organ segmentation.In order to fully exploit the strengths of convolutional neural network(CNN)in local information extraction and transformers in model-ing long-range dependency,we employ U-Net and Swin-Unet to construct the dual-branch network.The innovation of this method lies in the shuffling operation of high-dimensional features extracted at multiple stages from different branches of the network.It efficiently integrates local and global information by means of a dual-branch channel cross-fusion,enhanc-ing information interaction between the dual-branch network at different stages.This addresses the limitation in segmenta-tion accuracy caused by the blurring of object contours in images.Additionally,to address the challenge of great scale varia-tion among multiple organs,we introduce a new gated decoder based on multi-scale feature(GDMF)to extract multi-scale high-dimensional features at different stages of the network and perform adaptive feature enhancement,and adopts the atten-tion mechanisms and feature mappings to assist in acquiring accurate target information.The experimental results on auto-mated cardiac diagnosis challenge(ACDC)and fast and low GPU memory abdominal organ segmentation challenge 2021(FLARE21)datasets demonstrate that our proposed method outperforms existing mainstream medical image segmentation methods and effectively solves the problems of the great scale variation in target sizes and blurred boundary in medical images.
作者
雷涛
张峻铭
杜晓刚
闵重丹
杨子瑶
LEI Tao;ZHANG Jun-ming;DU Xiao-gang;MIN Chong-dan;YANG Zi-yao(School of Electronic Information and Artificial Intelligence,Shaanxi University of Science and Technology,Xi’an,Shaanxi 710021,China;Shaanxi Joint Laboratory of Artificial Intelligence,Shaanxi University of Science and Technology,Xi’an,Shaanxi 710021,China)
出处
《电子学报》
CSCD
北大核心
2024年第12期4142-4152,共11页
Acta Electronica Sinica
基金
国家自然科学基金(No.62271296,No.62201334)
陕西省杰出青年科学基金(No.2021JC-47)
陕西省重点研发计划(No.2022GY-436,No.2021ZDLGY08-07)
陕西省创新能力支撑计划(No.2020SS-03)
陕西省教育厅科学研究计划项目(No.23JP014,No.23JP022)。
