A 3D Cross-Modality Feature Interaction Network With Volumetric Feature Alignment for Brain Tumor and Tissue Segmentation

计算机科学 人工智能 特征(语言学) 分割 模态(人机交互) 模式识别(心理学) 卷积神经网络 计算机视觉 特征提取 语言学 哲学
作者
Yuzhou Zhuang,Hong Liu,Enmin Song,Chih‐Cheng Hung
出处
期刊:IEEE Journal of Biomedical and Health Informatics [Institute of Electrical and Electronics Engineers]
卷期号:27 (1): 75-86 被引量:40
标识
DOI:10.1109/jbhi.2022.3214999
摘要

Accurate volumetric segmentation of brain tumors and tissues is beneficial for quantitative brain analysis and brain disease identification in multi-modal Magnetic Resonance (MR) images. Nevertheless, due to the complex relationship between modalities, 3D Fully Convolutional Networks (3D FCNs) using simple multi-modal fusion strategies hardly learn the complex and nonlinear complementary information between modalities. Meanwhile, the indiscriminative feature aggregation between low-level and high-level features easily causes volumetric feature misalignment in 3D FCNs. On the other hand, the 3D convolution operations of 3D FCNs are excellent at modeling local relations but typically inefficient at capturing global relations between distant regions in volumetric images. To tackle these issues, we propose an Aligned Cross-Modality Interaction Network (ACMINet) for segmenting the regions of brain tumors and tissues from MR images. In this network, the cross-modality feature interaction module is first designed to adaptively and efficiently fuse and refine multi-modal features. Secondly, the volumetric feature alignment module is developed for dynamically aligning low-level and high-level features by the learnable volumetric feature deformation field. Thirdly, we propose the volumetric dual interaction graph reasoning module for graph-based global context modeling in spatial and channel dimensions. Our proposed method is applied to brain glioma, vestibular schwannoma, and brain tissue segmentation tasks, and we performed extensive experiments on BraTS2018, BraTS2020, Vestibular Schwannoma, and iSeg-2017 datasets. Experimental results show that ACMINet achieves state-of-the-art segmentation performance on all four benchmark datasets and obtains the highest DSC score of hard-segmented enhanced tumor region on the validation leaderboard of the BraTS2020 challenge.
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
刚刚
宝贝完成签到,获得积分10
1秒前
轻狂书生完成签到,获得积分10
1秒前
kjbt发布了新的文献求助10
1秒前
xxx_oo完成签到,获得积分10
4秒前
科研通AI2S应助老八的嘴采纳,获得10
4秒前
充电宝应助古炮采纳,获得10
4秒前
乖猫要努力完成签到,获得积分10
6秒前
iceeer完成签到,获得积分10
6秒前
Fawn完成签到 ,获得积分10
8秒前
8秒前
李心澍关注了科研通微信公众号
8秒前
9秒前
苹果初阳完成签到,获得积分10
9秒前
223311完成签到,获得积分10
10秒前
lbx完成签到,获得积分10
10秒前
wzxhhh完成签到,获得积分10
10秒前
毕个业完成签到 ,获得积分10
10秒前
一只橙子完成签到,获得积分10
10秒前
甜甜的采蓝完成签到 ,获得积分10
11秒前
Wguan完成签到,获得积分10
11秒前
huangxiaoniu完成签到,获得积分10
12秒前
12秒前
成就钧完成签到,获得积分10
13秒前
Rondab应助zzzkyt采纳,获得10
14秒前
跳跃的翼发布了新的文献求助10
14秒前
马华化完成签到,获得积分0
14秒前
李琛完成签到,获得积分10
14秒前
Xide发布了新的文献求助10
14秒前
温婉的香水完成签到 ,获得积分10
14秒前
KKA完成签到,获得积分20
15秒前
burn完成签到,获得积分10
16秒前
弱于一般人类完成签到,获得积分10
16秒前
戴好头盔搞科研完成签到,获得积分10
16秒前
feishao完成签到,获得积分10
17秒前
17秒前
雯子完成签到,获得积分10
18秒前
火星上勒关注了科研通微信公众号
18秒前
Xl完成签到,获得积分10
18秒前
仙依依完成签到 ,获得积分10
19秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
Immigrant Incorporation in East Asian Democracies 600
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress 500
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 3968603
求助须知:如何正确求助?哪些是违规求助? 3513420
关于积分的说明 11168029
捐赠科研通 3248900
什么是DOI,文献DOI怎么找? 1794540
邀请新用户注册赠送积分活动 875187
科研通“疑难数据库(出版商)”最低求助积分说明 804676