对COVID-19的研究面临的挑战有:感染区域特征的高变异性(CT切片中感染的纹理、大小和位置变化较大,对检测具有挑战性);感染与正常组织之间的低强度对比(类间方差很小);数据量小导致训练困难。 论文提出Inf-Net从肺部CT图片中自动分割感染区域。其中,并行部分解码器(PPD)用于聚合高级特征(结合上下文信息)并生成全局图。
To resolve these issues, a new inf-Net (Lung Infection Segmentation Deep Network) is designed for detecting the affected areas from the CT images automatically. For the worst segmentation results, the Edge-Attention Representation (EAR) is optimized using Adaptive Donkey an...
"Towards Efficient COVID-19 CT Annotation: A Benchmark for Lung and Infection Segmentation."arXiv, 2020. arXiv:https://arxiv.org/abs/2004.12537 dataset:https://gitee.com/junma11/COVID-19-CT-Seg-Benchmark Jun Ma, Yixin Wang, Xingle An, Cheng Ge, Ziqi Yu, Jianan Chen, Qiongjie Zhu,...
A weakly supervised deep learning approach has been proposed for COVID-19 classification and lesion localization by using CT volumes [16]. Specifically, the lung region was first segmented with a pre-trained U-Net, then the segmented lung region was fed into a deep neural network for ...
Accurate segmentation of COVID-19 lesion is useful in evaluating the degree of lung infection and disease progression, and provides a foundation for follow-up therapy of patients. However, manual labeling of lesion is a time-consuming process, and precision is largely affected by the subjective ...
Lung Infection Segmentation Deep Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices. In our Inf-Net, a parallel partial decoder is used to aggregate the high-level features and generate a global map. Then, the implicit reverse attention and explicit ...
COVID-19 CT lung and infection segmentation project, annotated and verified by Nanjing Drum Tower Hospital (China)42 Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) (countries unknown)43 Open access online collaborative radiology resource (Radiopaedia) (countries un...
Inf-Net:从CT图像自动分割COVID-19肺部感染 2019年冠状病毒病(COVID-19)于2020年初在全球蔓延,导致世界面临生存性健康危机。从计算机断层扫描(CT)图像自动检测肺部感染提供了巨大的潜力,可以增强传统医疗策略来应对COVID-19。然而,从CT切片中分割感染区域面临若干挑战,包括感染特征的高差异以及感染与正常组织之间的低...
To precisely determine the severity of COVID-19-related pneumonia, computed tomography (CT) is an imaging modality beneficial for patient monitoring and therapy planning. Thus, we aimed to develop a deep learning-based image segmentation model to automatically assess lung lesions related to COVID-19...
To address these challenges, a novel COVID-19 Lung Infection Segmentation Deep Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices. In our Inf-Net, a parallel partial decoder is used to aggregate the high-level features and generate a global map. Then...