Brain Glioma Segmentation with Multi-modal and Multi-scale Magnetic Resonance Imaging
To achieve precise segmentation of small target regions of glioma,a multi-modal and multi-scale MRI glioma seg-mentation model is proposed.The image features of each modality are obtained through the multi-modal feature extraction module,which enhances the reusability of the feature information by the network.The multi-scale feature fusion module is used to learn key features at different scales,and improve the feature recognition ability of the network for small target glioma regions.A weighted hy-brid loss function is used to address the class imbalance problem.The proposed model is tested on the BraTS(brain tumor segmenta-tion)2019 dataset,where the Dice scores of the whole tumor,tumor core,and enhancing tumor are 0.857,0.869 and 0.878,and Hausdorff distances are 2.543,1.583 and 1.526,respectively.The experimental results show that the model can effectively improve the segmentation accuracy of small target regions of glioma.
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