Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes

卷积神经网络 胎头 人工智能 分类 超声波 计算机科学 深度学习 模式识别(心理学) 医学 胎儿 机器学习 放射科 怀孕 生物 遗传学
作者
Xavier P. Burgos-Artizzu,David Coronado-Gutiérrez,B. Valenzuela‐Alcaraz,Elisenda Bonet-Carné,Elisenda Eixarch,F. Crispi,E. Gratacós
出处
期刊:Scientific Reports [Nature Portfolio]
卷期号:10 (1) 被引量:87
标识
DOI:10.1038/s41598-020-67076-5
摘要

The goal of this study was to evaluate the maturity of current Deep Learning classification techniques for their application in a real maternal-fetal clinical environment. A large dataset of routinely acquired maternal-fetal screening ultrasound images (which will be made publicly available) was collected from two different hospitals by several operators and ultrasound machines. All images were manually labeled by an expert maternal fetal clinician. Images were divided into 6 classes: four of the most widely used fetal anatomical planes (Abdomen, Brain, Femur and Thorax), the mother's cervix (widely used for prematurity screening) and a general category to include any other less common image plane. Fetal brain images were further categorized into the 3 most common fetal brain planes (Trans-thalamic, Trans-cerebellum, Trans-ventricular) to judge fine grain categorization performance. The final dataset is comprised of over 12,400 images from 1,792 patients, making it the largest ultrasound dataset to date. We then evaluated a wide variety of state-of-the-art deep Convolutional Neural Networks on this dataset and analyzed results in depth, comparing the computational models to research technicians, which are the ones currently performing the task daily. Results indicate for the first time that computational models have similar performance compared to humans when classifying common planes in human fetal examination. However, the dataset leaves the door open on future research to further improve results, especially on fine-grained plane categorization.

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
李青的大表姐完成签到 ,获得积分10
刚刚
刚刚
1秒前
小星完成签到,获得积分10
1秒前
sue完成签到,获得积分10
2秒前
2秒前
欣慰的凡儿完成签到,获得积分10
2秒前
2秒前
luria发布了新的文献求助10
2秒前
Owen应助单车采纳,获得30
2秒前
妖哥发布了新的文献求助10
2秒前
青争完成签到,获得积分10
2秒前
活力友容发布了新的文献求助20
2秒前
3秒前
3秒前
秋秋大王发布了新的文献求助10
4秒前
SPU的小追随完成签到,获得积分10
4秒前
try完成签到,获得积分10
4秒前
章节发布了新的文献求助10
4秒前
5秒前
鲨鱼完成签到 ,获得积分10
5秒前
5秒前
5秒前
6秒前
落卿然完成签到,获得积分20
6秒前
崔诗云发布了新的文献求助10
6秒前
6秒前
赘婿应助醉熏的笑萍采纳,获得10
6秒前
啤酒完成签到,获得积分10
7秒前
天侠客完成签到,获得积分10
8秒前
温柔灵波完成签到 ,获得积分10
8秒前
lalala发布了新的文献求助10
8秒前
sciscisci发布了新的文献求助10
8秒前
猪猪hero发布了新的文献求助10
9秒前
unique发布了新的文献求助10
9秒前
量子星尘发布了新的文献求助10
10秒前
落卿然发布了新的文献求助10
10秒前
齐羽发布了新的文献求助10
11秒前
12秒前
乐乐应助旺旺小小酥采纳,获得50
13秒前
高分求助中
Picture Books with Same-sex Parented Families: Unintentional Censorship 700
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
Effective Learning and Mental Wellbeing 300
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 3974463
求助须知:如何正确求助?哪些是违规求助? 3518823
关于积分的说明 11196212
捐赠科研通 3255008
什么是DOI,文献DOI怎么找? 1797655
邀请新用户注册赠送积分活动 877052
科研通“疑难数据库(出版商)”最低求助积分说明 806130