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C3D-UNET: A COMPREHENSIVE 3D UNET FOR COVID-19 SEGMENTATION WITH INTACT ENCODING AND LOCAL ATTENTION

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收录情况： ◇ CPCI(ISTP) ◇ EI

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机构： [1]The school of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China [2]The college of Computer Science and Technology, Zhejiang University, China [3]The Department of Radiology, First Affiliated Hospital to Amy Medical University, Chongqing, China

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关键词： 3D COVID-19 segmentation CT image analysis deep learning

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For COVID-19 prevention and treatment, it is essential to screen the pneumonia lesions in the lung region and analyze them in a qualitative and quantitative manner. Three-dimensional (3D) computed tomography (CT) volumes can provide sufficient information; however, extra boundaries of the lesions are also needed. The major challenge of automatic 3D segmentation of COVID-19 from CT volumes lies in the inadequacy of datasets and the wide variations of pneumonia lesions in their appearance, shape, and location. In this paper, we introduce a novel network called Comprehensive 3D UNet (C3D-UNet). Compared to 3D-UNet, an intact encoding (IE) strategy designed as residual dilated convolutional blocks with increased dilation rates is proposed to extract features from wider receptive fields. Moreover, a local attention (LA) mechanism is applied in skip connections for more robust and effective information fusion. We conduct five-fold cross-validation on a private dataset and independent offline evaluation on a public dataset. Experimental results demonstrate that our method outperforms other compared methods.

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第一作者机构： [1]The school of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China

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