[1]陈菁菁,李小霞,吕念祖.结合通道权重更新与密集残差金字塔空间注意力的皮肤病变分割方法[J].中国医学物理学杂志,2023,40(1):39-46.[doi:DOI:10.3969/j.issn.1005-202X.2023.01.007]
　CHEN Jingjing,LI Xiaoxia,L?Nianzu,et al.Skin lesion segmentation method combining channel weight update and dense residual pyramid spatial attention[J].Chinese Journal of Medical Physics,2023,40(1):39-46.[doi:DOI:10.3969/j.issn.1005-202X.2023.01.007]
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结合通道权重更新与密集残差金字塔空间注意力的皮肤病变分割方法()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
40卷
期数:
2023年第1期
页码:
39-46
栏目:
医学影像物理
出版日期:
2023-01-07

文章信息/Info

Title:
Skin lesion segmentation method combining channel weight update and dense residual pyramid spatial attention
文章编号:
1005-202X（2023）01-0039-08
作者:
陈菁菁1; 李小霞1; 2; 吕念祖1
1.西南科技大学信息工程学院， 四川 绵阳 621010； 2.特殊环境机器人技术四川省重点实验室， 四川 绵阳 621000
Author(s):
CHEN Jingjing1;  LI Xiaoxia1;  2;  L?Nianzu1
1. School of Information Engineering, Southwest University of Science and Technology, Mianyang 621000, China 2. Sichuan Key Laboratory of Robotics in Special Environment, Mianyang 621000, China
关键词:
皮肤病变分割; U-Net; 注意力机制; 边界损失函数
Keywords:
skin lesion segmentation U-Net attention mechanism boundary loss function
分类号:
R318；TP391
DOI:
DOI:10.3969/j.issn.1005-202X.2023.01.007
文献标志码:
A
摘要:
皮肤病变分割是计算机辅助诊断黑色素瘤的关键步骤。为了精确提取出皮肤病变区域，本研究基于U-Net提出一种新的皮肤病变分割方法。该方法引入通道权重更新模块和密集残差金字塔空间注意力模块，分别从通道和空间上提取有效信息，突出病变特征，抑制无关特征，从而提升网络对病变区域的分割精度；此外，构造了一种加权边界损失函数，通过对病变轮廓进行强监督，减少病变边缘特征的丢失。实验表明在ISIC 2018和PH2皮肤镜图像数据集中，该方法的Dice系数分别达到了91.3%、92.2%，相比U-Net提升了5.0%、4.3%。
Abstract:
The segmentation of skin lesions is critical for the computer-aided diagnosis of melanoma. A novel skin lesion segmentation method is proposed based on U-Net for extracting skin lesions more accurately. The proposed method adopts the channel weight update module and the dense residual pyramid spatial attention module to extract effective information from channels and space, highlight lesion features and suppress irrelevant features, thereby improving the accuracy of the network for the segmentation of pathological regions. In addition, a weighted boundary loss function is constructed to reduce the loss of lesion edge features through strong supervision on lesion contours. The experiment results show that the proposed method achieves Dice coefficients of 91.3% and 92.2% on ISIC 2018 and PH2 dermoscopic image data sets, respectively, which are improved by 5.0% and 4.3% as compared with U-Net.

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备注/Memo

备注/Memo:
【收稿日期】2022-07-20 【基金项目】国家自然科学基金（62071399），四川省科技计划（2023YFG0262, 2021YFG0383） 【作者简介】陈菁菁，硕士，研究方向：深度学习、医学图像处理，E-mail: 1322327218@qq.com 【通信作者】李小霞，博士，硕士生导师，教授，研究方向：模式识别、图像处理，E-mail: 664368504@qq.com
更新日期/Last Update: 2023-01-07