南京大学学报（自然科学版）2025，Vol.61Issue(4)：635-644,10.DOI:10.13232/j.cnki.jnju.2025.04.009
融合图像域与K空间域特征的MRI脑肿瘤分割方法
MRI brain tumor segmentation by fusing image-domain and K-space domain features
摘要
Abstract
Magnetic resonance imaging(MRI)is paramount for clinical diagnosis and treatment of brain tumours.However,extant methods primarily focus on feature extraction and fusion from MR images(image domain),with a paucity of research exploring different feature extraction approaches.This paper proposes an MRI brain tumour segmentation method integrating features from both the image domain and the K-space domain.The proposed method exploits the global properties of the K-space domain in MRI to achieve independent extraction of global features.The method consists of an image domain feature extraction module,a K-space domain feature extraction module,an adaptive affine fusion module,and a decoder module.First,MR images and K-space domain data are separately input to two feature extraction modules to extract local and global features.Then,the adaptive affine fusion module establishes an affine mechanism to effectively fuse the two types of features.Finally,the decoder,enhanced by deep supervision,generates the final segmentation mask by exploiting the fused feature information.The proposed method was evaluated on the BraTS public brain tumour dataset,and compared to other methods,it achieved improvements of 1.12%～2.47%in Dice coefficient and 17.5%～52.8%in HD95 metric.In addition,the proposed method demonstrated excellent performance in terms of computational complexity,thus suitability for clinical diagnostic applications.关键词
脑肿瘤分割/自适应融合/特征提取/卷积神经网络/深度学习Key words
brain tumor segmentation/adaptive fusion/feature extraction/Convolutional Neural Network/deep learning分类
信息技术与安全科学引用本文复制引用
许崇彩,卞聪超,王国富..融合图像域与K空间域特征的MRI脑肿瘤分割方法[J].南京大学学报（自然科学版）,2025,61(4):635-644,10.基金项目
国家重点研发计划"政府间国际科技创新合作"重点专项项目(2022YFE013460),宿迁市科技计划项目(S202218,K202205),江苏省产学研合作项目(BY20231250,BY20231210) （2022YFE013460）
