A hybrid convolutional network for medical image segmentation
Robust segmentation of organs from medical images isone of the key techniques in medical image analysis for disease diagnosis.U-Net is a robust structure for medical image segmentation.However,U-Net uses a continuous downsampling encoder to capture multi-scale features,and the high-level semantic features are not sufficiently recovered,which leads to the loss of contextual information and fails to adequately recover the organ features to be segmented.In this paper,a new hybrid convolu-tional network is proposed to capture more contextual information and high-level semantic features.The main idea of the hybrid convolutional network is to extract more contextual information and high-level semantic features from the feature encoder using the proposed hybrid convolutional connectivity module.The multi-scale feature extraction module is used to connect the encoder and decoder sub-networks to obtain richer multi-scale feature maps.The proposed method is compared with the state-of-the-art methods on CHASEDB dataset and FRSA dataset.The experimental results show that the proposed method outperforms other segmentation methods.
medical image segmentationhybrid convolutionalmulti-scale featurecontextual information
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