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!

Multi-modal brain tumor segmentation via conditional synthesis with Fourier domain adaptation

分割 计算机科学 人工智能 合成数据 模式识别(心理学) 图像分割 人工神经网络 基本事实 计算机视觉
作者
Yasmina Al Khalil,Aymen Ayaz,Cristian Lorenz,Jürgen Weese,Josien P. W. Pluim,Marcel Breeuwer
出处
期刊:Computerized Medical Imaging and Graphics [Elsevier BV]
卷期号:112: 102332-102332 被引量:2
标识
DOI:10.1016/j.compmedimag.2024.102332
摘要

Accurate brain tumor segmentation is critical for diagnosis and treatment planning, whereby multi-modal magnetic resonance imaging (MRI) is typically used for analysis. However, obtaining all required sequences and expertly labeled data for training is challenging and can result in decreased quality of segmentation models developed through automated algorithms. In this work, we examine the possibility of employing a conditional generative adversarial network (GAN) approach for synthesizing multi-modal images to train deep learning-based neural networks aimed at high-grade glioma (HGG) segmentation. The proposed GAN is conditioned on auxiliary brain tissue and tumor segmentation masks, allowing us to attain better accuracy and control of tissue appearance during synthesis. To reduce the domain shift between synthetic and real MR images, we additionally adapt the low-frequency Fourier space components of synthetic data, reflecting the style of the image, to those of real data. We demonstrate the impact of Fourier domain adaptation (FDA) on the training of 3D segmentation networks and attain significant improvements in both the segmentation performance and prediction confidence. Similar outcomes are seen when such data is used as a training augmentation alongside the available real images. In fact, experiments on the BraTS2020 dataset reveal that models trained solely with synthetic data exhibit an improvement of up to 4% in Dice score when using FDA, while training with both real and FDA-processed synthetic data through augmentation results in an improvement of up to 5% in Dice compared to using real data alone. This study highlights the importance of considering image frequency in generative approaches for medical image synthesis and offers a promising approach to address data scarcity in medical imaging 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
专一的咖啡完成签到,获得积分10
3秒前
Akim应助hume采纳,获得10
8秒前
yuan完成签到 ,获得积分10
15秒前
akakns完成签到 ,获得积分10
25秒前
罐罐完成签到,获得积分10
27秒前
六六完成签到 ,获得积分10
31秒前
32秒前
34秒前
直觉应助科研达人采纳,获得10
35秒前
Cain完成签到,获得积分10
35秒前
叮当拉卡给叮当拉卡的求助进行了留言
37秒前
和谐的冬莲完成签到 ,获得积分10
37秒前
Li发布了新的文献求助10
39秒前
水若琳发布了新的文献求助10
39秒前
隐形曼青应助背后的鞋垫采纳,获得10
42秒前
lab完成签到 ,获得积分0
46秒前
wch666完成签到,获得积分10
46秒前
GingerF完成签到,获得积分0
51秒前
NZC完成签到,获得积分10
52秒前
luroa完成签到 ,获得积分10
54秒前
英俊的铭应助风吹屁屁凉采纳,获得10
56秒前
科研通AI2S应助科研通管家采纳,获得10
57秒前
科目三应助wch666采纳,获得10
57秒前
轻语完成签到 ,获得积分10
1分钟前
张建威完成签到,获得积分10
1分钟前
bkagyin应助小刘采纳,获得10
1分钟前
1分钟前
调皮的灰狼完成签到,获得积分10
1分钟前
1分钟前
litieniu发布了新的文献求助10
1分钟前
1分钟前
感谢有你完成签到 ,获得积分10
1分钟前
1分钟前
CodeCraft应助科研达人采纳,获得10
1分钟前
1分钟前
小刘发布了新的文献求助10
1分钟前
1分钟前
dou完成签到 ,获得积分10
1分钟前
默默完成签到,获得积分10
1分钟前
1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 3990008
求助须知:如何正确求助?哪些是违规求助? 3532034
关于积分的说明 11256121
捐赠科研通 3270913
什么是DOI,文献DOI怎么找? 1805105
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 809216