Joint segmentation and detection of COVID-19 via a sequential region generation network.
Joint segmentation and detection of COVID-19 via a sequential region generation network.
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DOI：
10.1016/j.patcog.2021.108006
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发表时间：
2021-10
影响因子：
8
通讯作者：
Ji R
中科院分区：
文献类型：
--
作者：
Wu J;Zhang S;Li X;Chen J;Xu H;Zheng J;Gao Y;Tian Y;Liang Y;Ji R
冠状病毒病（COVID-19）的快速流行对全球公共卫生造成了毁灭性影响。为了治疗这种疾病，医学成像成为一种有用的诊断工具。然而，COVID-19的计算机断层扫描（CT）诊断需要专家丰富的临床经验。因此，实现COVID-19的快速准确分割和检测至关重要。本文提出了一种简单而高效的通用网络，称为顺序区域生成网络（SRGNet），用于联合检测和分割COVID-19的病变区域。SRGNet可以充分利用监督分割信息，然后输出多尺度分割预测。通过这样，可以在预测的分割图上生成高质量的病变区域建议，从而降低诊断成本。同时，检测结果反过来通过后处理过程细化分割图，从而显着提高分割精度。我们的SRGNet相对于最先进的方法的优势通过对已建立的COVID-19数据库的广泛实验进行了验证。
The fast pandemics of coronavirus disease (COVID-19) has led to a devastating influence on global public health. In order to treat the disease, medical imaging emerges as a useful tool for diagnosis. However, the computed tomography (CT) diagnosis of COVID-19 requires experts’ extensive clinical experience. Therefore, it is essential to achieve rapid and accurate segmentation and detection of COVID-19. This paper proposes a simple yet efficient and general-purpose network, called Sequential Region Generation Network (SRGNet), to jointly detect and segment the lesion areas of COVID-19. SRGNet can make full use of the supervised segmentation information and then outputs multi-scale segmentation predictions. Through this, high-quality lesion-areas suggestions can be generated on the predicted segmentation maps, reducing the diagnosis cost. Simultaneously, the detection results conversely refine the segmentation map by a post-processing procedure, which significantly improves the segmentation accuracy. The superiorities of our SRGNet over the state-of-the-art methods are validated through extensive experiments on the built COVID-19 database.
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影响因子：
3.2
作者：
Khehrah N;Farid MS;Bilal S;Khan MH
通讯作者：
Khan MH
DOI：
10.1007/s10489-020-01714-3
发表时间：
2020-04-22
期刊：
Applied Intelligence (Dordrecht, Netherlands)
影响因子：
--
作者：
Butt C;Gill J;Chun D;Babu BA
通讯作者：
Babu BA
影响因子：
3.5
作者：
Boldsen JK;Engedal TS;Pedraza S;Cho TH;Thomalla G;Nighoghossian N;Baron JC;Fiehler J;Østergaard L;Mouridsen K
通讯作者：
Mouridsen K
影响因子：
19.7
作者：
Bernheim, Adam;Mei, Xueyan;Chung, Michael
通讯作者：
Chung, Michael
影响因子：
4.4
作者：
Apostolopoulos, Ioannis D.;Mpesiana, Tzani A.
通讯作者：
Mpesiana, Tzani A.