Reliable Thyroid Carcinoma Detection with Real-Time Intelligent Analysis of Ultrasound Images

甲状腺结节 计算机科学 结核(地质) 人工智能 卷积神经网络 超声波 模式识别(心理学) 计算机视觉 放射科 甲状腺 医学 古生物学 内科学 生物
作者
Fang Han,Li Gong,Yuan Xu,Yiyao Zhuo,Wentao Kong,Chenglei Peng,Jie Yuan
出处
期刊:Ultrasound in Medicine and Biology [Elsevier BV]
卷期号:47 (3): 590-602 被引量:11
标识
DOI:10.1016/j.ultrasmedbio.2020.11.024
摘要

Thyroid carcinoma is one of the most common endocrine diseases globally, and the incidence has been on the rise in recent years. Ultrasound imaging is the primary clinical method for early thyroid nodule diagnosis. Regions of interest (ROIs) of nodules in ultrasound images are difficult to detect because of their irregular shape nand vague margins. Accurate real-time thyroid nodule detection can provide ROIs for subsequent nodule diagnosis automatically, avoid variabilities between the subjective interpretations and inter-observer effectively and alleviate the workloads of medical practitioners. The aim of this study was to present a reliable, real-time detection method based on the Faster R-CNN (region-based convolutional network) framework for accurate and fast detection of thyroid nodules in ultrasound images. Our study proposed a faster and more accurate thyroid nodule detection method based on the Faster R-CNN framework by adding three strategies: feature pyramid, spatial remapping and anchor-box redesign. Specifically, the network takes raw ultrasound images as inputs and generates boxes with positions and the possibilities that these boxes contain thyroid nodules. The proposed method could locate and detect target nodules accurately with a mean average precision of 92.79% with more than 9000 patient images. In addition, the detection rate has accelerated to >16 frames per second, four times faster than that of the initial network. Therefore, it can meet the requirements of clinical application. The performance of the fivefold cross-validation was also accurate and robust. The proposed automatic thyroid nodule detection method yields better performance in accuracy and detection speed, which indicates the potential value of our method in assisting clinical ultrasound image interpretation.
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
1秒前
zhui完成签到,获得积分10
2秒前
RESLR完成签到,获得积分10
3秒前
3秒前
3秒前
用户5063899发布了新的文献求助10
3秒前
5秒前
雨的痕迹完成签到,获得积分10
6秒前
Loik发布了新的文献求助10
6秒前
充电宝应助绛川采纳,获得10
6秒前
量子星尘发布了新的文献求助10
8秒前
8秒前
CAOHOU应助sue采纳,获得10
9秒前
9秒前
Loik完成签到,获得积分10
12秒前
12秒前
苗条梦玉发布了新的文献求助10
13秒前
13秒前
13秒前
18秒前
绛川发布了新的文献求助10
19秒前
momo发布了新的文献求助10
20秒前
搜集达人应助潘善若采纳,获得10
20秒前
yyer发布了新的文献求助10
21秒前
实心小墩墩完成签到,获得积分10
23秒前
28秒前
29秒前
香蕉觅云应助su采纳,获得10
29秒前
深情安青应助momo采纳,获得10
31秒前
31秒前
32秒前
可爱的函函应助hu采纳,获得10
34秒前
34秒前
35秒前
ABS发布了新的文献求助10
35秒前
36秒前
FashionBoy应助忘记时间采纳,获得30
37秒前
爆米花应助无情的匪采纳,获得10
38秒前
39秒前
刘寄奴发布了新的文献求助10
40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 3989297
求助须知:如何正确求助?哪些是违规求助? 3531418
关于积分的说明 11253893
捐赠科研通 3270097
什么是DOI,文献DOI怎么找? 1804884
邀请新用户注册赠送积分活动 882087
科研通“疑难数据库(出版商)”最低求助积分说明 809158