[1]裴潇倜,吕琳,黄鹏杰,等.基于U-Net的T细胞斑点检测方法研究[J].中国医学物理学杂志,2021,38(4):518-522.[doi:DOI:10.3969/j.issn.1005-202X.2021.04.022]
 PEI Xiaoti,L?Lin,HUANG Pengjie,et al.T-cell spot test based on U-Net[J].Chinese Journal of Medical Physics,2021,38(4):518-522.[doi:DOI:10.3969/j.issn.1005-202X.2021.04.022]
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基于U-Net的T细胞斑点检测方法研究()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
38卷
期数:
2021年第4期
页码:
518-522
栏目:
医学人工智能
出版日期:
2021-04-29

文章信息/Info

Title:
T-cell spot test based on U-Net
文章编号:
1005-202X(2021)04-0518-05
作者:
裴潇倜吕琳黄鹏杰陈兆学林勇
上海理工大学医疗器械与食品学院, 上海 200093
Author(s):
PEI Xiaoti L?Lin HUANG Pengjie CHEN Zhaoxue LIN Yong
School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
关键词:
T细胞斑点检测深度学习U-Net网络图像分割
Keywords:
Keywords: T-cell spot test deep learning U-Net network image segmentation
分类号:
R318;TP391
DOI:
DOI:10.3969/j.issn.1005-202X.2021.04.022
文献标志码:
A
摘要:
针对传统图像分割方法抗噪性弱、容易漏检的问题,提出基于U-Net模型的T细胞斑点分割算法。通过中值滤波器平滑消除噪声,灰度化处理降低背景干扰,采用Adam算法优化损失函数,能有效提高分割准确率。实验结果表明,与基于区域生长的传统分割方法对比,U-Net方法在少量斑点和较多斑点两种情况下F1分别提升9%和6%,验证了其有效性。
Abstract:
Abstract: Aiming at the problems of weak noise resistance and high probability of missed detection in traditional image segmentation method, a T-cell spot segmentation algorithm based on U-Net model is proposed. The segmentation accuracy is effectively improved by smoothing noises through median filter, reducing background interference by graying, and taking Adam algorithm as a loss function. The experimental results show that, compared with the traditional segmentation method based on region growth, the proposed method based on U-Net increases F1 by 9% and 6% in the experiments with a small number of spots and lots of spots, which verifies its effectiveness.

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备注/Memo

备注/Memo:
【收稿日期】2020-12-17 【基金项目】国家自然科学基金(31301092) 【作者简介】裴潇倜,硕士研究生,研究方向:医学信息工程,E-mail: peixiaoti@163.com 【通信作者】林勇,副教授,研究方向:医学信息工程,E-mail: yong_lynn@163.com
更新日期/Last Update: 2021-04-29