亲爱的研友该休息了!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!身体可是革命的本钱,早点休息,好梦!

Effective hybrid attention network based on pseudo-color enhancement in ultrasound image segmentation

计算机科学 人工智能 分割 特征(语言学) 卷积神经网络 模式识别(心理学) 频道(广播) 背景(考古学) 图像分割 计算机视觉 计算机网络 语言学 生物 哲学 古生物学
作者
Xuping Huang,Qian Wang,Junxi Chen,Lingna Chen,Zhiyi Chen
出处
期刊:Image and Vision Computing [Elsevier BV]
卷期号:137: 104742-104742 被引量:12
标识
DOI:10.1016/j.imavis.2023.104742
摘要

Ultrasound image segmentation plays a vital role in the early diagnosis of human diseases. It helps diagnose many diseases, such as breast cancer, hemangioma, and other gynecological disorders. However, the intrinsic imaging characteristics of ultrasound images result in substantially lower resolution and clarity than CT, MRI, and other imaging modalities, and they are sensitive to interference from external influences. With its inherent artifacts, blurred lesion boundaries, and uneven intensity distribution, ultrasound images present a challenging task when it comes to segmenting lesion areas accurately. In recent years, convolutional neural networks (CNNs) have achieved remarkable results in medical image segmentation tasks. However, CNNs are limited in capturing the remote dependencies of the input image, leading to degraded accuracy in segmenting ultrasound lesions. In this paper, we developed a deep convolutional neural network that incorporates the pseudo-color enhancement algorithm and hybrid attention modules that enhance the network’s ability to extract fine features and remote modeling capabilities. We propose a novel multi-scale channel attention-based decoder that efficiently uses the feature maps from the encoder as a complement and fuses them with the upsampled feature maps. The hybrid attention combination captures cross-channel interactions efficiently and enhances the context modeling capability, further improving the extraction of coarse and delicate features, and resulting in significant performance improvements. We found that the dice performance improved by 2.54%, 2.47%, 1.39%, 0.99%, and 1.23% on the BUL, BUSI, Hemangioma, BP, and VUI. Results from four public datasets and one self-collected dataset indicate that the proposed method outperforms other medical image segmentation methods for ultrasound image lesion segmentation.
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
pegasus0802完成签到 ,获得积分10
2秒前
5823364完成签到,获得积分10
20秒前
automan完成签到,获得积分10
22秒前
天亮polar完成签到,获得积分10
23秒前
59秒前
1分钟前
1分钟前
朴实云应发布了新的文献求助10
1分钟前
林子青完成签到,获得积分10
1分钟前
核桃发布了新的文献求助30
2分钟前
李健应助reerwt采纳,获得10
2分钟前
2分钟前
ICSSCI发布了新的文献求助10
2分钟前
2分钟前
刘宇童给刘宇童的求助进行了留言
2分钟前
reerwt发布了新的文献求助10
2分钟前
2分钟前
科研通AI2S应助科研通管家采纳,获得10
2分钟前
科研通AI2S应助科研通管家采纳,获得10
2分钟前
ICSSCI完成签到,获得积分10
2分钟前
3分钟前
董可以发布了新的文献求助10
3分钟前
风华正茂完成签到,获得积分10
3分钟前
3分钟前
4分钟前
jimmy_bytheway完成签到,获得积分0
4分钟前
桃桃发布了新的文献求助10
4分钟前
可爱的函函应助桃桃采纳,获得10
4分钟前
4分钟前
4分钟前
4分钟前
4分钟前
NexusExplorer应助科研通管家采纳,获得10
4分钟前
所所应助爱笑的毛衣采纳,获得10
4分钟前
5分钟前
5分钟前
duan完成签到 ,获得积分10
5分钟前
holder完成签到,获得积分10
5分钟前
5分钟前
沐白发布了新的文献求助10
5分钟前
高分求助中
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
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 3990049
求助须知:如何正确求助?哪些是违规求助? 3532108
关于积分的说明 11256369
捐赠科研通 3270998
什么是DOI,文献DOI怎么找? 1805166
邀请新用户注册赠送积分活动 882270
科研通“疑难数据库(出版商)”最低求助积分说明 809228