Brain tumor image segmentation method based on multi-modal feature fusion(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2022年第6期
- Page:
- 682-689
- Research Field:
- 医学影像物理
- Publishing date:
Info
- Title:
- Brain tumor image segmentation method based on multi-modal feature fusion
- Author(s):
- FANG Xinlin; FANG Yanhong; WANG Di
- School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China
- Keywords:
- Keywords: multi-modal image brain tumor feature fusion medical image segmentation deep learning
- PACS:
- R318；TP391.4
- DOI:
- DOI:10.3969/j.issn.1005-202X.2022.06.005
- Abstract:
- Abstract: Aiming at the problem that most of the current medical image segmentation methods are difficult to perform feature fusion for multi-modal images to achieve accurate segmentation, a multi-modal brain tumor image feature fusion strategy based on encoder and decoder overall architecture is proposed. In the coding phase, twin networks are used to extract features from different modal data. The number of network parameters can be effectively reduced by sharing the structural parameters and weights of twin networks. In addition, the interstage fusion is added in the coding phase of feature extraction to keep the common features of different modes while emphasizing their complementary features. Then, the idea of dense skip connection is introduced in the decoding phase to maximize the combination of low-level details and high-level semantic information of feature maps at different scales. Finally, a mixed loss function is designed, so that the prediction graph generated by the network is supervised by the truth graph, and that the highest-level feature fusion graph is also supervised by the truth graph sampled under the same multiplier. The proposed method is tested on the public data set BraTS2019 and evaluated with 5 commonly used indexes for image segmentation. For the segmentations of brain tumor and edema area, the proposed method are superior to more advanced algorithms U-NET and PA-NET in many indexes, and the average Dice coefficient, positive prediction rate, sensitivity, Hausdorff distance and mean intersection over union of the proposed method are 0.884, 0.870, 0.898, 3.917 and 79.1%, respectively. The experimental results reveal that the addition of interstage fusion and interlayer skip connection can improve the segmentation performance of multimodal medical images, and it has important application value and theoretical significance in the segmentation of brain tumor in magnetic resonance image.
Last Update: 2022-06-27