计算机科学
人工智能
分割
编码器
块(置换群论)
编码(内存)
图像分割
计算机视觉
卷积神经网络
手术器械
背景(考古学)
模式识别(心理学)
操作系统
生物
机械工程
工程类
古生物学
数学
几何学
作者
Lei Yang,Yuge Gu,Gui‐Bin Bian,Yanhong Liu
出处
期刊:IEEE Journal of Biomedical and Health Informatics
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:: 1-13
标识
DOI:10.1109/jbhi.2023.3344716
摘要
Minimally invasive surgery, which relies on surgical robots and microscopes, demands precise image segmentation to ensure safe and efficient procedures. Nevertheless, achieving accurate segmentation of surgical instruments remains challenging due to the complexity of the surgical environment. To tackle this issue, this paper introduces a novel multiscale dual-encoding segmentation network, termed MSDE-Net, designed to automatically and precisely segment surgical instruments. The proposed MSDE-Net leverages a dual-branch encoder comprising a convolutional neural network (CNN) branch and a transformer branch to effectively extract both local and global features. Moreover, an attention fusion block (AFB) is introduced to ensure effective information complementarity between the dual-branch encoding paths. Additionally, a multilayer context fusion block (MCF) is proposed to enhance the network's capacity to simultaneously extract global and local features. Finally, to extend the scope of global feature information under larger receptive fields, a multi-receptive field fusion (MRF) block is incorporated. Through comprehensive experimental evaluations on two publicly available datasets for surgical instrument segmentation, the proposed MSDE-Net demonstrates superior performance compared to existing methods.
科研通智能强力驱动
Strongly Powered by AbleSci AI