[1]韩 悦,张永寿,郭依廷,等.乳腺癌腋窝淋巴结超声图像分割算法研究[J].南京师大学报(自然科学版),2021,44(04):122-126.[doi:10.3969/j.issn.1001-4616.2021.04.016]
　Han Yue,Zhang Yongshou,Guo Yiting,et al.Research on Ultrasound Image Segmentation Algorithm forAxillary Lymph Node with Breast Cancer[J].Journal of Nanjing Normal University(Natural Science Edition),2021,44(04):122-126.[doi:10.3969/j.issn.1001-4616.2021.04.016]
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乳腺癌腋窝淋巴结超声图像分割算法研究()
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《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

卷:
第44卷
期数:
2021年04期
页码:
122-126
栏目:
·计算机科学与技术·
出版日期:
2021-12-15

文章信息/Info

Title:
Research on Ultrasound Image Segmentation Algorithm forAxillary Lymph Node with Breast Cancer
文章编号:
1001-4616(2021)04-0122-05
作者:
韩 悦1; 2; 张永寿3; 郭依廷4; 班楷第2; 5; 丛金玉2; 5; 魏本征2; 5
(1.山东中医药大学智能与信息工程学院,山东 济南 250355)(2.山东中医药大学医学人工智能研究中心,山东 青岛 266112)(3.中国人民解放军第960医院医学工程科,山东 济南 250031)(4.山东中医药大学第二附属医院放射科,山东 济南 250001)(5.山东中医药大学青岛中医药科学院,山东 青岛 266112)
Author(s):
Han Yue1; 2; Zhang Yongshou3; Guo Yiting4; Ban Kaidi2; 5; Cong Jinyu2; 5; Wei Benzheng2; 5
(1.College of Intelligence and Information Engineering,Shandong University of Traditional Chinese Medicine,Jinan 250355,China)(2.Center for Medical Artificial Intelligence,Shandong University of Traditional Chinese Medicine,Qingdao 266112,China)(3.Medical Engineering Department,the 960th Hospital of the PLA,Jinan 250031,China)(4.Department of Radiology,The Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine,Jinan 250001,China)(5.Qingdao Academy of Chinese Medical Sciences,Shandong University of Traditional Chinese Medicine,Qingdao 266112,China)
关键词:
腋窝淋巴结; 超声图像; 语义分割; 图像分析; 深度学习
Keywords:
axillary lymph node; ultrasound image; semantic segmentation; image analysis; deep learning
分类号:
TP391.41 R737.9
DOI:
10.3969/j.issn.1001-4616.2021.04.016
文献标志码:
A
摘要:
腋窝淋巴结是乳腺癌常见的转移位置,超声图像是腋窝淋巴结转移的主要检查方式之一. 当前,腋窝淋巴结超声图像分割因其自身的噪声多、特征复杂等特点,使得其分割准确率还有待提高. 本文提出了一种基于腋窝超声的新型分割算法U-net-MDSC,该算法采用解码和编码的方式自动分割腋窝淋巴结,针对超声图像尺寸小、分辨率低的问题,减少了网络中不必要的下采样结构; 针对编码过程中语义信息丢失较多的特点,采用了密集跳连接结构来充分提取图像特征,可提供淋巴结的定位、大小等信息. 为检验算法的有效性,本文将提出的算法在356个病人的腋窝淋巴结超声图像上进行验证,结果显示算法在测试集上交并比达到了0.838,Dice系数达到了0.903.
Abstract:
Axillary lymph node is a common metastatic site of breast cancer,and ultrasound image is one of the main ways to detect axillary lymph node metastasis. At present,the segmentation accuracy of axillary lymph node ultrasound image needs to be improved due to its characteristics of much noise and complicate features. In this paper,a new segmentation algorithm based on axillary ultrasound,U-net-MDSC,is proposed. The algorithm automatically segments axillary lymph node by means of decoding and encoding. Aiming at the problems of small ultrasonic image size and low image quality,the unnecessary subsampling structure in the network is reduced. In view of the feature that semantic information is lost a lot in the coding process,a dense jump-join structure is used to extract the image features fully,which can provide information such as the location and size of lymph node. In order to test the effectiveness of the algorithm,the proposed algorithm was verified in the axillary lymph node ultrasound images of 356 patients,and the results showed that intersection over union and Dice coefficient of the proposed algorithm reached 0.838 and 0.903 on the test set.

参考文献/References:

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相似文献/References:

[1]班楷第,孙 宇,韩悦,等.乳腺癌超声图像的腋窝淋巴结特征指导分割网络[J].南京师大学报(自然科学版),2023,46(02):92.[doi:10.3969/j.issn.1001-4616.2023.02.012]
　Ban Kaidi,Sun Yu,Han Yue,et al.Features Guidance Axillary Lymph Nodes Network for Breast Cancer Segmentation[J].Journal of Nanjing Normal University(Natural Science Edition),2023,46(04):92.[doi:10.3969/j.issn.1001-4616.2023.02.012]

备注/Memo

备注/Memo:
收稿日期:2021-03-31.
基金项目:山东省自然科学基金重点项目(ZR2020KF013)、山东省自然科学基金重大基础研究项目(ZR2019ZD04、ZR2020ZD44).
通讯作者:张永寿,主任技师,研究方向:医疗设备工程. E-mail:2041831268@qq.com; 魏本征,教授,博士生导师,研究方向:医学图像处理. E-mail:wbz99@sina.com
更新日期/Last Update: 2021-12-15