[1]刘潇霜,张伟.采用融合ResNet和Transformer的U-Net进行疟疾感染红细胞分割[J].中国医学物理学杂志,2024,41(2):191-197.[doi:DOI:10.3969/j.issn.1005-202X.2024.02.011]
　LIU Xiaoshuang,ZHANG Wei.Segmentation of malaria-infected erythrocytes using U-Net incorporating Transformer and ResNet[J].Chinese Journal of Medical Physics,2024,41(2):191-197.[doi:DOI:10.3969/j.issn.1005-202X.2024.02.011]
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采用融合ResNet和Transformer的U-Net进行疟疾感染红细胞分割()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
41卷
期数:
2024年第2期
页码:
191-197
栏目:
医学影像物理
出版日期:
2024-03-13

文章信息/Info

Title:
Segmentation of malaria-infected erythrocytes using U-Net incorporating Transformer and ResNet
文章编号:
1005-202X（2024）02-0191-07
作者:
刘潇霜; 张伟
甘肃中医药大学信息工程学院， 甘肃 兰州 730000
Author(s):
LIU Xiaoshuang;  ZHANG Wei
School of Information Engineering, Gansu University of Chinese Medicine, Lanzhou 730000, China
关键词:
疟疾; U-Net; Transformer; 语义分割
Keywords:
malaria U-Net Transformer semantic segmentation
分类号:
R318；TP391
DOI:
DOI:10.3969/j.issn.1005-202X.2024.02.011
文献标志码:
A
摘要:
针对疟疾感染红细胞图像分割模型分割性能不高的问题，提出一种改进的U-Net网络模型，融合ResNet和Transformer。首先编码器部分使用ResNet，加深特征提取网络，以提取更深层次的特征；然后将ResNet输出传入Transformer模块进行目标区域特征的加强；最后通过解码器模块进行特征融合并输出结果。在疟疾显微图像数据集上，本文方法的Dice相似系数、平均交并比、类别平均像素准确率均优于U-Net网络，分别达到了87.40％、76.85％、85.28％。本文方法可以提高疟疾感染红细胞图像的分割精度，为疟疾诊断提供更有效和准确的解决方案。
Abstract:
A novel U-Net network model which integrates ResNet and Transformer is proposed to address the problem of poor malaria-in fected erythrocyte performance of the existing models. ResNet is used in the encoder to deepen the feature extraction network for extracting deeper features, and the ResNet output is inputted into Transformer module for the feature enhancement in the target area, and finally the decoder module is used to perform feature fusion and output the results. The experiment on the malaria microscopy image dataset shows that the proposed method outperforms U-Net in Dice similarity coefficient, mean intersection over union, and mean pixel accuracy, reaching 87.40%, 76.85%, and 85.28%, respectively. The proposed method can improve the accuracy of malaria-infected erythrocyte segmentation and provide a more effective and accurate solution for malaria diagnosis.

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备注/Memo

备注/Memo:
【收稿日期】2023-10-26 【基金项目】甘肃省教育厅创新基金（2022B-113） 【作者简介】刘潇霜，硕士研究生，研究方向：深度学习、医学图像处理，E-mail: 284385351@qq.com 【通信作者】张伟，副教授，硕士生导师，研究方向：医学信号处理、计算机仿真，E-mail: 4865354@qq.com
更新日期/Last Update: 2024-02-27