当前位置: X-MOL 学术Signal Process. Image Commun. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
MID-UNet: Multi-input directional UNet for COVID-19 lung infection segmentation from CT images
Signal Processing: Image Communication ( IF 3.5 ) Pub Date : 2022-08-02 , DOI: 10.1016/j.image.2022.116835
Jianning Chi 1 , Shuang Zhang 1 , Xiaoying Han 1 , Huan Wang 1 , Chengdong Wu 1 , Xiaosheng Yu 1
Affiliation  

Coronavirus Disease 2019 (COVID-19) has spread globally since the first case was reported in December 2019, becoming a world-wide existential health crisis with over 90 million total confirmed cases. Segmentation of lung infection from computed tomography (CT) scans via deep learning method has a great potential in assisting the diagnosis and healthcare for COVID-19. However, current deep learning methods for segmenting infection regions from lung CT images suffer from three problems: (1) Low differentiation of semantic features between the COVID-19 infection regions, other pneumonia regions and normal lung tissues; (2) High variation of visual characteristics between different COVID-19 cases or stages; (3) High difficulty in constraining the irregular boundaries of the COVID-19 infection regions. To solve these problems, a multi-input directional UNet (MID-UNet) is proposed to segment COVID-19 infections in lung CT images. For the input part of the network, we firstly propose an image blurry descriptor to reflect the texture characteristic of the infections. Then the original CT image, the image enhanced by the adaptive histogram equalization, the image filtered by the non-local means filter and the blurry feature map are adopted together as the input of the proposed network. For the structure of the network, we propose the directional convolution block (DCB) which consist of 4 directional convolution kernels. DCBs are applied on the short-cut connections to refine the extracted features before they are transferred to the de-convolution parts. Furthermore, we propose a contour loss based on local curvature histogram then combine it with the binary cross entropy (BCE) loss and the intersection over union (IOU) loss for better segmentation boundary constraint. Experimental results on the COVID-19-CT-Seg dataset demonstrate that our proposed MID-UNet provides superior performance over the state-of-the-art methods on segmenting COVID-19 infections from CT images.



中文翻译:

MID-UNet:用于从 CT 图像分割 COVID-19 肺部感染的多输入定向 UNet

自 2019 年 12 月报告首例病例以来,2019 年冠状病毒病 (COVID-19) 已在全球蔓延,已成为全球性的生存健康危机,确诊病例总数超过 9000 万。通过深度学习方法从计算机断层扫描 (CT) 扫描中分割肺部感染在协助 COVID-19 的诊断和医疗保健方面具有巨大潜力。然而,目前从肺部CT图像中分割感染区域的深度学习方法存在三个问题:(1)COVID-19感染区域、其他肺炎区域和正常肺组织之间的语义特征区分度低;(2) 不同 COVID-19 病例或阶段之间视觉特征的高度差异;(3) 限制COVID-19感染区域不规则边界的难度大。为了解决这些问题,提出了一种多输入定向 UNet (MID-UNet) 来分割肺部 CT 图像中的 COVID-19 感染。对于网络的输入部分,我们首先提出了一个图像模糊描述符来反映感染的纹理特征。然后将原始CT图像、自适应直方图均衡增强的图像、非局部均值滤波器滤波的图像和模糊特征图一起作为所提出网络的输入。对于网络结构,我们提出了由 4 个方向卷积核组成的方向卷积块 (DCB)。DCB 应用于快捷连接以在将提取的特征传输到反卷积部分之前对其进行细化。此外,我们提出了一种基于局部曲率直方图的轮廓损失,然后将其与二元交叉熵(BCE)损失和并集交集(IOU)损失相结合,以获得更好的分割边界约束。COVID-19-CT-Seg 数据集的实验结果表明,我们提出的 MID-UNet 在从 CT 图像中分割 COVID-19 感染方面提供了优于最先进方法的性能。

更新日期:2022-08-02
down
wechat
bug