已入深夜,您辛苦了!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,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

Regional perception and multi-scale feature fusion network for cardiac segmentation

计算机科学 比例(比率) 分割 感知 人工智能 融合 模式识别(心理学) 计算机视觉 特征(语言学) 地图学 地理 心理学 神经科学 语言学 哲学
作者
Chenggang Lu,Jinli Yuan,Kewen Xia,Zhitao Guo,Muxuan Chen,Hengyong Yu
出处
期刊:Physics in Medicine and Biology [IOP Publishing]
卷期号:68 (10): 105003-105003 被引量:6
标识
DOI:10.1088/1361-6560/acc71f
摘要

Objective.Cardiovascular disease (CVD) is a group of diseases affecting cardiac and blood vessels, and short-axis cardiac magnetic resonance (CMR) images are considered the gold standard for the diagnosis and assessment of CVD. In CMR images, accurate segmentation of cardiac structures (e.g. left ventricle) assists in the parametric quantification of cardiac function. However, the dynamic beating of the heart renders the location of the heart with respect to other tissues difficult to resolve, and the myocardium and its surrounding tissues are similar in grayscale. This makes it challenging to accurately segment the cardiac images. Our goal is to develop a more accurate CMR image segmentation approach.Approach.In this study, we propose a regional perception and multi-scale feature fusion network (RMFNet) for CMR image segmentation. We design two regional perception modules, a window selection transformer (WST) module and a grid extraction transformer (GET) module. The WST module introduces a window selection block to adaptively select the window of interest to perceive information, and a windowed transformer block to enhance global information extraction within each feature window. The WST module enhances the network performance by improving the window of interest. The GET module grids the feature maps to decrease the redundant information in the feature maps and enhances the extraction of latent feature information of the network. The RMFNet further introduces a novel multi-scale feature extraction module to improve the ability to retain detailed information.Main results.The RMFNet is validated with experiments on three cardiac data sets. The results show that the RMFNet outperforms other advanced methods in overall performance. The RMFNet is further validated for generalizability on a multi-organ data set. The results also show that the RMFNet surpasses other comparison methods.Significance.Accurate medical image segmentation can reduce the stress of radiologists and play an important role in image-guided clinical procedures.
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
vivid发布了新的文献求助10
刚刚
Sienna完成签到,获得积分10
3秒前
hyhyhyhy发布了新的文献求助10
5秒前
CipherSage应助wyp采纳,获得10
9秒前
wkjfh举报怕黑的亦瑶求助涉嫌违规
9秒前
yahonyoyoyo发布了新的文献求助10
11秒前
slycmd完成签到,获得积分10
11秒前
风清扬应助咋取名字采纳,获得10
12秒前
Tough完成签到 ,获得积分10
15秒前
16秒前
17秒前
19秒前
落羽发布了新的文献求助10
20秒前
Rondab应助一颗椰子糖耶采纳,获得10
20秒前
wyp发布了新的文献求助10
22秒前
22秒前
Ava应助大大怪采纳,获得10
24秒前
华仔应助落羽采纳,获得10
24秒前
Hyh_orz发布了新的文献求助30
25秒前
25秒前
大模型应助科研通管家采纳,获得10
25秒前
FashionBoy应助科研通管家采纳,获得10
26秒前
科研通AI5应助科研通管家采纳,获得10
26秒前
Rondab应助科研通管家采纳,获得30
26秒前
映城应助科研通管家采纳,获得30
26秒前
26秒前
26秒前
火翟丰丰山心完成签到,获得积分10
28秒前
29秒前
Orange应助XU采纳,获得10
31秒前
momo发布了新的文献求助30
32秒前
35秒前
香蕉觅云应助俏皮的白柏采纳,获得10
35秒前
纸包鱼发布了新的文献求助30
37秒前
Rondab应助一颗椰子糖耶采纳,获得10
39秒前
燕子发布了新的文献求助10
39秒前
40秒前
Hello应助小猪佩奇采纳,获得10
41秒前
脆脆鲨完成签到,获得积分10
42秒前
彭于晏应助PMoLGGYM2021采纳,获得10
42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 3989811
求助须知:如何正确求助?哪些是违规求助? 3531927
关于积分的说明 11255560
捐赠科研通 3270706
什么是DOI,文献DOI怎么找? 1805035
邀请新用户注册赠送积分活动 882181
科研通“疑难数据库(出版商)”最低求助积分说明 809190