清晨好,您是今天最早来到科研通的研友!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您科研之路漫漫前行!

An image registration-based self-supervised Su-Net for carotid plaque ultrasound image segmentation

人工智能 分割 计算机科学 深度学习 特征(语言学) 模式识别(心理学) 任务(项目管理) 图像配准 图像(数学) 医学 计算机视觉 语言学 哲学 经济 管理
作者
Jing Ding,Ran Zhou,Xiaoyue Fang,Furong Wang,Ji Wang,Haitao Gan,Aaron Fenster
出处
期刊:Computer Methods and Programs in Biomedicine [Elsevier BV]
卷期号:244: 107957-107957 被引量:2
标识
DOI:10.1016/j.cmpb.2023.107957
摘要

Total Plaque Area (TPA) measurement is critical for early diagnosis and intervention of carotid atherosclerosis in individuals with high risk for stroke. The delineation of the carotid plaques is necessary for TPA measurement, and deep learning methods can automatically segment the plaque and measure TPA from carotid ultrasound images. A large number of labeled images is essential for training a good deep learning model, but it is very difficult to collect such large labeled datasets for carotid image segmentation in clinical practice. Self-supervised learning can provide a possible solution to improve the deep-learning models on small labeled training datasets by designing a pretext task to pre-train the models without using the segmentation masks. However, the existing self-supervised learning methods do not consider the feature presentations of object contours. In this paper, we propose an image registration-based self-supervised learning method and a stacked U-Net (SSL-SU-Net) for carotid plaque ultrasound image segmentation, which can better exploit the semantic features of carotid plaque contours in self-supervised task training. Our network was trained on different numbers of labeled images (n = 10, 33, 50 and 100 subjects) and tested on 44 subjects from the SPARC dataset (n = 144, London, Canada). The network trained on the entire SPARC dataset was then directly applied to an independent dataset collected in Zhongnan hospital (n = 497, Wuhan, China). For the 44 subjects tested on the SPARC dataset, our method yielded a DSC of 80.25-89.18% and the produced TPA measurements, which were strongly correlated with manual segmentation (r = 0.965-0.995, ρ< 0.0001). For the Zhongnan dataset, the DSC was 90.3% and algorithm TPAs were strongly correlated with manual TPAs (r = 0.985, ρ< 0.0001). The results demonstrate that our proposed method yielded excellent performance and good generalization ability when trained on a small labeled dataset, facilitating the use of deep learning in carotid ultrasound image analysis and clinical practice. The code of our algorithm is available https://github.com/a610lab/Registration-SSL.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
lty完成签到,获得积分20
2秒前
孟寐以求完成签到 ,获得积分10
3秒前
4秒前
fkdbdy发布了新的文献求助10
9秒前
朴子完成签到 ,获得积分10
13秒前
香蕉觅云应助勇往直前采纳,获得10
18秒前
勇往直前完成签到,获得积分10
22秒前
widesky777完成签到 ,获得积分0
22秒前
carne完成签到,获得积分10
23秒前
24秒前
上善若水完成签到 ,获得积分10
27秒前
勇往直前发布了新的文献求助10
28秒前
Tumbleweed668完成签到,获得积分20
54秒前
科研狗完成签到 ,获得积分0
56秒前
科研通AI2S应助科研通管家采纳,获得10
59秒前
Dongjie完成签到,获得积分10
1分钟前
蔡勇强完成签到 ,获得积分10
1分钟前
雷小牛完成签到 ,获得积分10
1分钟前
培培完成签到 ,获得积分10
1分钟前
ikun0000完成签到,获得积分10
1分钟前
午后狂睡完成签到 ,获得积分10
1分钟前
emxzemxz完成签到 ,获得积分10
1分钟前
yellowonion完成签到 ,获得积分10
1分钟前
岁月如歌完成签到,获得积分0
1分钟前
英俊的铭应助过眼云烟采纳,获得10
1分钟前
重重重飞完成签到 ,获得积分10
2分钟前
theo完成签到 ,获得积分10
2分钟前
allrubbish完成签到,获得积分10
2分钟前
凤兮完成签到 ,获得积分10
2分钟前
2分钟前
过眼云烟发布了新的文献求助10
2分钟前
之_ZH完成签到 ,获得积分10
2分钟前
淡淡菠萝完成签到 ,获得积分10
2分钟前
大饼完成签到 ,获得积分10
2分钟前
2分钟前
酷酷的紫南完成签到 ,获得积分10
2分钟前
Ggap1发布了新的文献求助10
2分钟前
你的笑慌乱了我的骄傲完成签到 ,获得积分10
2分钟前
应夏山完成签到 ,获得积分10
2分钟前
JasonTrue完成签到,获得积分10
2分钟前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
Immigrant Incorporation in East Asian Democracies 600
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3968532
求助须知:如何正确求助?哪些是违规求助? 3513358
关于积分的说明 11167309
捐赠科研通 3248700
什么是DOI,文献DOI怎么找? 1794453
邀请新用户注册赠送积分活动 875030
科研通“疑难数据库(出版商)”最低求助积分说明 804664