[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):92-98.[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(02):92-98.[doi:10.3969/j.issn.1001-4616.2023.02.012]
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乳腺癌超声图像的腋窝淋巴结特征指导分割网络()
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《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

卷:
第46卷
期数:
2023年02期
页码:
92-98
栏目:
计算机科学与技术
出版日期:
2023-06-15

文章信息/Info

Title:
Features Guidance Axillary Lymph Nodes Network for Breast Cancer Segmentation
文章编号:
1001-4616(2023)02-0092-07
作者:
班楷第1; 2; 3; 孙 宇2; 3; 韩悦2; 3; 魏本征2; 3
(1.山东中医药大学中医药创新研究院,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.山东中医药大学青岛中医药科学院,山东 青岛 266112)
Author(s):
Ban Kaidi1; 2; 3; Sun Yu2; 3; Han Yue2; 3; Wei Benzheng2; 3
(1.College of Innovative Institute of Chinese Medicine and Pharmacy,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.Qingdao Academy of Chinese Medical Sciences,Shandong University of Traditional Chinese Medicine,Qingdao 266112,China)
关键词:
腋窝淋巴结; 图像分割; 深度学习; 超声图像; 分割网络
Keywords:
Axillary lymph nodes;  image segmentation;  deep learning;  ultrasonic images;  segmentation network
分类号:
TP391
DOI:
10.3969/j.issn.1001-4616.2023.02.012
文献标志码:
A
摘要:
腋窝淋巴结超声图像分割是一项具有临床价值且存在挑战性的任务,对乳腺癌的诊断具有重要意义. 为提升腋窝淋巴结临床分割精度,针对腋窝淋巴结超声图像特点,本文在编码器—解码器架构基础上,设计特征指导模块,实现特征提取中的特征高效融合和系数探索,并在此基础上提出腋窝淋巴结特征指导分割网络,实现超声图像中腋窝淋巴结的精准识别与分割. 实验表明,本文算法在712张腋窝淋巴结超声图像数据集上的m-ACC为0.977,m-IoU为0.878,m-Dice为0.932,优于现有分割模型,分割结果可作为临床诊断参考,辅助乳腺癌腋窝淋巴结转移的精准诊断.
Abstract:
Ultrasound image segmentation of axillary lymph nodes is a clinically valuable and challenging task, which is of great significance for the diagnosis of breast cancer. In order to improve the clinical segmentation accuracy of axillary lymph nodes, Aiming at the characteristics of axillary lymph nodes ultrasonic images, on the encoder decoder architecture, this paper designs a feature guidance module to achieve efficient feature fusion and coefficient exploration in feature extraction, and on this basis, the features guidance network of axillary lymph nodes is proposed to achieve accurate identification and segmentation of axillary lymph nodes in ultrasound images. Experiments show that on the dataset of 712 axillary lymph node ultrasound images, the m-ACC of this algorithm is 0.977, the m-IoU score is 0.878, and the m-Dice can reach 0.932, which is better than the existing segmentation model, and the segmentation results can be used as a clinical diagnosis reference to assist in the accurate diagnosis of axillary lymph node metastasis in breast cancer.

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备注/Memo

备注/Memo:
收稿日期:2022-09-20.
基金项目:国家自然科学基金项目(61872225)、山东省自然科学基金项目(ZR2020KF013、ZR2020ZD44、ZR2019ZD04、ZR2020QF043)、山东省高校青创引才育才计划项目(2019-173)、齐鲁卫生与健康领军人才培育工程项目.
通讯作者:魏本征,博士,教授,博士生导师,研究方向:医学人工智能,机器学习等. E-mail:wbz99@sina.com
更新日期/Last Update: 2023-06-15