Zhengfeng Bao
ABSTRACT
The imaging data of Multi-modal brain has been put to a wide use in the identification of epilepsy and tumor lesions because it can provide multi-information about a target (epilepsy lesion, tumor, or tissue). However, most of the existing multi-modal medical image segmentation networks in the existing multi-modal segmentation schemes, on the one hand, adopt the input-level fusion strategy, which directly integrates the multi-modal images in the original input space. But it is hard to preserve the modality-specific properties. On the other hand, the decision-level fusion method with the use of the single network can better exploit the unique information of the corresponding modality, but with the problem that the features learned from a single modality cannot be easily combined with the features of other modality. In this paper, we have proposed Multi-Encoder with Hybrid Lateral Connection Network (MEHLC-Net), a semantic segmentation network based on 2D images with multi encoder structure for multi-modal MRI tumor sub-region segmentation. We use hybrid lateral connections instead of long connections in the U-Net structure to extract features, which can overcome the difficulty of highorder feature fusion caused by multiple encoders. We combine cross-connection with self-attention mechanisms to enhance the accuracy of the network. We evaluate our method on the multimodal data set of BRATS. Compared with the advanced No-New-Net, our model improves the TC of the DSC indicator by 0.13% and the ET indicator by 0.58% respectively.
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Index Terms
- Multi-modal Brain Image Segmentation Based on Multi-Encoder with Hybrid Lateral Connection (MEHLC-Net)
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