TNSNet: Thyroid nodule segmentation in ultrasound imaging using soft shape supervision

计算机科学 分割 人工智能 甲状腺结节 计算机视觉 深度学习 可视化 反褶积 结核(地质) 模式识别(心理学) 图像分割 路径(计算) 散斑噪声 人工神经网络 斑点图案 甲状腺 医学 算法 古生物学 内科学 生物 程序设计语言
作者
Jiawei Sun,Chunying Li,Zhengda Lu,Mu He,Tong Zhao,Xiaoqin Li,Liugang Gao,Kai Xie,Tao Lin,Jianfeng Sui,Qianyi Xi,Fan Zhang,Xinye Ni
出处
期刊:Computer Methods and Programs in Biomedicine [Elsevier BV]
卷期号:215: 106600-106600 被引量:41
标识
DOI:10.1016/j.cmpb.2021.106600
摘要

Thyroid nodules are a common disorder of the endocrine system. Segmentation of thyroid nodules on ultrasound images is an important step in the evaluation and diagnosis of nodules and an initial step in computer-aided diagnostic systems. The accuracy and consistency of segmentation remain a challenge due to the low contrast, speckle noise, and low resolution of ultrasound images. Therefore, the study of deep learning-based algorithms for thyroid nodule segmentation is important. This study utilizes soft shape supervision to improve the performance of detection and segmentation of boundaries of nodules. Soft shape supervision can emphasize the boundary features and assist the network in segmenting nodules accurately.We propose a dual-path convolution neural network, including region and shape paths, which use DeepLabV3+ as the backbone. Soft shape supervision blocks are inserted between the two paths to implement cross-path attention mechanisms. The blocks enhance the representation of shape features and add them to the region path as auxiliary information. Thus, the network can accurately detect and segment thyroid nodules.We collect 3786 ultrasound images of thyroid nodules to train and test our network. Compared with the ground truth, the test results achieve an accuracy of 95.81% and a DSC of 85.33. The visualization results also suggest that the network has learned clear and accurate boundaries of the nodules. The evaluation metrics and visualization results demonstrate the superior segmentation performance of the network to other classical deep learning-based networks.The proposed dual-path network can accurately realize automatic segmentation of thyroid nodules on ultrasound images. It can also be used as an initial step in computer-aided diagnosis. It shows superior performance to other classical methods and demonstrates the potential for accurate segmentation of nodules in clinical applications.
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
QQ发布了新的文献求助10
1秒前
无情心情完成签到,获得积分10
2秒前
无情心情发布了新的文献求助10
4秒前
老大蒂亚戈应助潇湘雪月采纳,获得10
5秒前
打我呀发布了新的文献求助30
5秒前
6秒前
所所应助科研通管家采纳,获得10
6秒前
汉堡包应助科研通管家采纳,获得10
7秒前
MchemG应助科研通管家采纳,获得10
7秒前
YamDaamCaa应助科研通管家采纳,获得30
7秒前
852应助科研通管家采纳,获得10
7秒前
大模型应助科研通管家采纳,获得30
7秒前
MchemG应助科研通管家采纳,获得10
7秒前
完美世界应助科研通管家采纳,获得30
7秒前
情怀应助科研通管家采纳,获得10
7秒前
科研通AI5应助科研通管家采纳,获得10
7秒前
隐形曼青应助科研通管家采纳,获得10
7秒前
MchemG应助科研通管家采纳,获得10
7秒前
7秒前
李爱国应助深情的雁露采纳,获得10
7秒前
8秒前
盘尼西林发布了新的文献求助10
13秒前
幸福大白发布了新的文献求助10
13秒前
希望天下0贩的0应助李李采纳,获得10
14秒前
QQ完成签到,获得积分20
14秒前
14秒前
BiuBiu怪完成签到,获得积分10
16秒前
Dellamoffy完成签到,获得积分10
16秒前
17秒前
cnlt应助会撒娇的采蓝采纳,获得10
17秒前
海藻发布了新的文献求助10
19秒前
20秒前
盘尼西林完成签到,获得积分10
20秒前
yar应助JK采纳,获得10
22秒前
xxddw发布了新的文献求助10
22秒前
23秒前
打我呀完成签到,获得积分10
23秒前
gsj发布了新的文献求助10
26秒前
zzk完成签到 ,获得积分10
26秒前
老大蒂亚戈应助潇湘雪月采纳,获得10
26秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
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 3989406
求助须知:如何正确求助?哪些是违规求助? 3531522
关于积分的说明 11254187
捐赠科研通 3270174
什么是DOI,文献DOI怎么找? 1804901
邀请新用户注册赠送积分活动 882105
科研通“疑难数据库(出版商)”最低求助积分说明 809174