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- W3204354322 abstract "The analysis of hippocampus morphology based on magnetic resonance images is of great significance in the diagnosis of Alzheimer’s disease and other neurological diseases, where accurate segmentation of the hippocampus is the premise. In this paper, a novel HPCSeg-Net was proposed for hippocampus segmentation based on the U-Net framework, which adopted a cascaded autofocus attention mechanism and adaptive feature recombination and recalibration module. The cascaded autofocus attention mechanism, which includes autofocus block and attention block, was devised for skip connection and semantic gap reduction between contracting and expansive pathways. The autofocus block was to generate powerful features by parallelizing multiple convolutional layers with different dilation rates, while the followed attention block was to suppress irrelevant areas around the hippocampus and highlighting the salient features. Moreover, feature recombination employed linear expansion and compression to generate complex semantic segmentation features, while the feature recalibration module was to collect context information and preserve spatial information. The experiments implemented on 133 studies from the ADNI dataset demonstrated that the proposed HPCSeg-Net could achieve competitive segmentation performance (average Dice index > 0.89) compared to multi-atlas segmentation methods." @default.
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- W3204354322 date "2021-01-01" @default.
- W3204354322 modified "2023-09-26" @default.
- W3204354322 title "HPCSeg-Net: Hippocampus Segmentation Network Integrating Autofocus Attention Mechanism and Feature Recombination and Recalibration Module" @default.
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- W3204354322 doi "https://doi.org/10.1007/978-3-030-87358-5_63" @default.
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