文献类型：期刊文献

英文题名：Deep fusion of multi-modal features for brain tumor image segmentation

作者：Zhang, Guying[1];Zhou, Jia[2];He, Guanghua[1,3];Zhu, Hancan[1,3]

机构：[1]Shaoxing Univ, Sch Math Phys & Informat, Shaoxing 312000, Zhejiang, Peoples R China;[2]Zhejiang Prov Peoples Hosp, Affiliated Peoples Hosp, Hangzhou Med Coll, Canc Ctr,Gamma Knife Treatment Ctr, Hangzhou 310014, Zhejiang, Peoples R China;[3]Shaoxing Univ, Inst Artificial Intelligence, Shaoxing 312000, Zhejiang, Peoples R China

年份：2023

卷号：9

期号：8

外文期刊名：HELIYON

收录：SCI-EXPANDED(收录号：WOS:001088215700001)、、Scopus(收录号：2-s2.0-85168116425)、WOS

基金：This work was supported by Scientific Research Project of Shaoxing University (20210038) , National Natural Science Foundation of China (61602307) , and Zhejiang Provincial Natural Science Foundation of China (LY19F020013) .

语种：英文

外文关键词：Brain tumor segmentation; Deep convolutional network; Multi-modality fusion; Deep residual learning

外文摘要：Accurate segmentation of pathological regions in brain magnetic resonance images (MRI) is essential for the diagnosis and treatment of brain tumors. Multi-modality MRIs, which offer diverse feature information, are commonly utilized in brain tumor image segmentation. Deep neural networks have become prevalent in this field; however, many approaches simply concatenate different modalities and input them directly into the neural network for segmentation, disregarding the unique characteristics and complementarity of each modality. In this study, we propose a brain tumor image segmentation method that leverages deep residual learning with multi-modality image feature fusion. Our approach involves extracting and fusing distinct and complementary features from various modalities, fully exploiting the multi-modality information within a deep convolutional neural network to enhance the performance of brain tumor image segmentation. We evaluate the effectiveness of our proposed method using the BraTS2021 dataset and demonstrate that deep residual learning with multi-modality image feature fusion significantly improves segmentation accuracy. Our method achieves competitive segmentation results, with Dice values of 83.3, 89.07, and 91.44 for enhanced tumor, tumor core, and whole tumor, respectively. These findings highlight the potential of our method in improving brain tumor diagnosis and treatment through accurate segmentation of pathological regions in brain MRIs.