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Multimodal medical image segmentation using multi-scale context- aware network
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DOI：10.1016/j.neucom.2021.11.017.png)
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Multimodal medical image segmentation with different imaging devices is a key but challenging task in medical image visual analysis and reasoning. Recently, U-Net based networks achieved considerable suc-cess in semantic segmentation of medical image. However, U-Net utilizes a skip-connection to connect two symmetric encoder and decoder layers. Although the single granularity information of the encoder layer is preserved through skip connection, the rich multi-scale spatial information is ignored, which greatly affects its performance in the segmentation task. In this paper, a multi-scale context-aware net-work (CA-Net) for multimodal medical image segmentation is proposed, which captures rich context information with dense skip connection and assigns distinct weights to different channels. CA-Net con-sists of four key components, namely encoder module, multi-scale context fusion (MCF) module, decoder module, and dense skip connection module. The proposed MCF module extracts multi-scale spatial infor-mation through a spatial context fusion (SCF) block, and learn to balance channel-wise features through a Squeeze-and-Excitation (SE) block. Extensive experiments demonstrate that our model achieves state-of -the-art performance on three benchmark datasets of different modalities, including skin lesion segmen-tation in dermoscopy, lung segmentation in CT images, and blood vessel segmentation in retina images.(c) 2021 Published by Elsevier B.V.
Keyword:
Medical image segmentation
Atrous convolution
Dense skip connection
Multi-scale context fusion
Multimodality
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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