摘要
医学图像分割在计算机辅助诊断和手术导航等临床应用中起着至关重要的作用,旨在从复杂的医学影像中精准提取不同器官和病灶。然而,现有的U型网络结构在实际应用中存在跳跃连接信息冗余大和计算量高等问题。为了解决这些问题,提出一种轻量化医学图像分割网络ES-TransUNet(Efficient channel attention and Simple-TransUNet)。该网络在编码器中通过引入十字交叉注意力(CCA)机制捕捉图像中的长距离依赖关系,并优化Transformer中的多头注意力结构,从而使模型轻量化,在解码器中引入动态上采样(Dysample)模块提升上采样效率;同时为了减少跳跃连接中的信息冗余,引入简单上下文Transformer(SCOT)块对冗余特征进行过滤。在Synapse多器官分割和ACDC数据集上的实验结果表明,ES-TransUNet相比TransUNet分别取得了2.37和1.57个百分点的Dice相似系数(DSC)提升,并在Synapse数据集上使Hausdorff距离(HD)降低了约9.69。此外,所提网络与现有最先进的医学分割模型的对比结果表明,ES-TransUNet在保持较高分割精度的基础上,显著降低了模型的参数量和计算复杂度,并提高了推理效率。可见,该网络更满足实时医学图像分割的实际需求。
Medical image segmentation plays a crucial role in clinical applications such as computer-aided diagnosis and surgical navigation,aiming to extract different organs and lesions from complex medical images accurately.However,the existing U-shaped network architecture suffers from the problems such as high information redundancy in skip connections and high computational complexity.To address these challenges,a lightweight medical image segmentation network named ES-TransUNet(Efficient channel attention and Simple-TransUNet)was proposed.In the network,the Criss-Cross Attention(CCA)mechanism was introduced in the encoder to capture long-range dependencies and the multi-head attention structure in Transformer was optimized,so as to lighten the model.Dynamic upsampling(Dysample)module was introduced in the decoder to improve upsampling efficiency.At the same time,in order to reduce the information redundancy in skip connections,the Simple COntextual Transformer(SCOT)block was introduced to filter out redundant features.Experimental results on the Synapse multi-organ segmentation and ACDC datasets demonstrate that ES-TransUNet achieves 2.37 and 1.57 percentage points improvements,respectively,in Dice Similarity Coefficient(DSC)compared to TransUNet;and reduces the Hausdorff Distance(HD)by 9.69 approximately on the Synapse dataset.Additionally,the results of comparing proposed network with state-of-the-art medical segmentation models indicate that ES-TransUNet maintains high segmentation accuracy while reducing model parameters and computational complexity significantly,and improves inference efficiency.It can be seen that ES-TransUNet is more satisfied the practical requirements in real-time medical image segmentation.
作者
邓酩
徐锦凡
肖洪祥
谢晓兰
DENG Ming;XU Jinfan;XIAO Hongxiang;XIE Xiaolan(Guangxi Key Laboratory of Embedded Technology and Intelligent Systems(Guilin University of Technology),Guilin Guangxi 541006,China;College of Computer Science and Engineering,Guilin University of Technology,Guilin Guangxi 541006,China)
出处
《计算机应用》
北大核心
2025年第12期4037-4044,共8页
journal of Computer Applications
基金
国家自然科学基金资助项目(62262011)
广西重点研发计划项目(桂科AB23049001)。
