Abstract:
Automatic segmentation of the kidney and tumor from computed tomography (CT) images is an essential step in precision oncology and personalized treatment planning. Due to...Show MoreMetadata
Abstract:
Automatic segmentation of the kidney and tumor from computed tomography (CT) images is an essential step in precision oncology and personalized treatment planning. Due to the irregular shapes and vague boundaries of kidney and tumor, this is a challenging task. Most of existing methods focused on local features without fully considering the associations between regions and contextual relationships between features. We propose a new segmentation method, CR-UNet, to extract, encode and adaptively integrate multiple layers of relevant features. Since the semantic features of different channels contribute differently to the segmentation of kidney and tumor, we introduce semantic attention mechanism of channels. The regional association attention mechanism is established to integrate the semantic and positional connections between different regions. Ablation studies demonstrate the contributions of semantic associations between deep learning channels, and regional relation modelling. Comparison results with state-of-the-art methods over public dataset demonstrated improved tumor and kidney segmentation performance.
Published in: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Date of Conference: 01-05 November 2021
Date Added to IEEE Xplore: 09 December 2021
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PubMed ID: 34891893