IMAGE segmentationCOVID-19LUNGSMATHEMATICAL optimizationDONKEYSCOVID'19 has caused the entire universe to be in existential health crisis by spreading globally in the year 2020. The lungs infection is detected in Computed Tomography (CT) images which provide the best way to ...
图3.6COVID-19CTSegmentation数据集上感染区域分割的视觉效果比较28 表3.3COVID-19CTSegmentation数据集上感染区域分割的定量结果29 图3.7COVID-CS数据集上感染区域分割的视觉效果比较29 表3.4COVID-CS数据集上感染区域分割的定量结果30 表3.5GFNET的消融实验31 图4.1DEA-UNet模型的结构图,由一系列密集的边界注意力模...
Segmentation and Quantification Method forCT-based COVID-19 Diagnosis”。 核酸检测作为诊断COVID-19的黄金标准,对于早期的病人来说有着很高的假阴性率(falsenegative rate,FNR)。CT成像技术能更好地用于诊断阳性COVID-19,作者在文章中提出的方法实现了对不同来源的CT扫描感染区域进行分割和量化。首先,作者通过拟合...
it is challenging to segment infected regions in CT slices because the infected regions are multi-scale, and the boundary is not clear due to the low contrast between the infected area and the normal area. In this paper, a coarse refine segmentation network...
"COVID-19 CT segmentation dataset" Link:https://medicalsegmentation.com/covid19/ "Fighting Covid-19 Challenge - A platform for open research on large Covid-19 imaging datasets" Link:https://www.covid19challenge.eu "COVID-19: CASISTICA RADIOLOGICA ITALIANA" ...
T. Mahmud et al., "CovTANet: A Hybrid Tri-level Attention Based Network for Lesion Segmentation, Diagnosis, and Severity Prediction of COVID-19 Chest CT Scans," in IEEE Transactions on Industrial Informatics, doi: 10.1109/TII.2020.3048391....
在COVID-SemiSeg和真实CT体积上进行广泛实验表明,Inf-Net性能优于大多数尖端分割模型,并提高了当前的性能水平。 作者指出其动机源于临床医生在肺部感染检测过程中,首先对感染区域进行粗略定位,然后根据局部症状准确提取其轮廓。因此,我们认为区域和边界是区分正常组织和感染的两个关键特征。因此,我们的网络首先预测粗糙区...
Segmentation of infections from CT scans is important for accurate diagnosis and follow-up in tackling the COVID-19. Although the convolutional neural network has great potential to automate the segmentation task, most existing deep learning-based infection segmentation methods require fully annotated gro...
Experimental results obtained on a relatively large-scale CT segmentation dataset of around 900 images, incorporating this new regularization term leads to a 2% gain on overall segmentation performance compared to the Unet trained from scratch. Our experimental analysis, ranging from visual evaluation of...
今天给大家介绍的是阿联酋阿布扎比人工智能研究院范登平教授课题组发表在“IEEE T MED IMAGING”上的一篇文章” Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images”。应对COVID-19的传统医疗策略能力有限,作者提出了一种新的COVID-19肺部感染模型Inf-Net用于自动识别CT胸部切片感染区域,克服了...