In this study, we leverage 7T MR multi-modality and deep neural networks for accurate and efficient segmentation of the thalamus. Our contributions are 1) to build a dual-pathway and feature pyramid scheme to simultaneously encode global contextual information and local details within an encoder-decoder network; 2) to learn the optimal combination of global and local attentions to increase the feature representation power by adaptively recalibrating feature maps in an end-to-end manner. The proposed framework shows state-of-the-art performance on segmentation of the thalamus with 7T multi-modal MRI in an automatic and efficient way.
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