Automatic Lung Segmentation Based on Texture and Deep Features of HRCT Images with Interstitial Lung Disease

Pang, Ting; Guo, Shaoyong; Xin-wang, Zhang; Zhao, Lijie

doi:10.1155/2019/2045432

Cited by 36 publications

(36 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, radiologists lack a computerized tool to accurately quantify the severity of COVID-19, for example, the percentage of infection in the whole lung. In the literature, deep learning has become a popular method in medical image analysis and has been used in analyzing diffuse lung diseases on CT. 35,36 In this work, we explored deep learning to segment COVID-19 infection regions within lungs on CT images. The accurate segmentation provides quantitative information that is necessary to track disease progression and analyze longitude changes of COVID-19 during the entire treatment period.…”

Section: Discussionmentioning

confidence: 99%

Abnormal lung quantification in chest CT images of COVID‐19 patients with deep learning and its application to severity prediction

et al. 2021

View full text Add to dashboard Cite

Objective: CT provides rich diagnosis and severity information of COVID-19 in clinical practice. However, there is no computerized tool to automatically delineate COVID-19 infection regions in chest CT scans for quantitative assessment in advanced applications such as severity prediction. The aim of this study is to develop a deep learning (DL) based method for automatic segmentation and quantification of infection regions as well as the entire lungs from chest CT scans. Methods: The DL-based segmentation method employs the "VB-Net" neural network to segment COVID-19 infection regions in CT scans. The developed DL-based segmentation system is trained by CT scans from 249 COVID-19 patients, and further validated by CT scans from other 300 COVID-19 patients. To accelerate the manual delineation of CT scans for training, a human-involved-model-iterations (HIMI) strategy is also adopted to assist radiologists to refine automatic annotation of each 8 9

show abstract

Section: Discussionmentioning

confidence: 99%

Abnormal lung quantification in chest CT images of COVID‐19 patients with deep learning and its application to severity prediction

et al. 2021

View full text Add to dashboard Cite

show abstract

“…With the development of deep learning [11]- [15], the technology has a wide range of applications in medical image processing, including disease diagnosis [16], organ segmentation [17], etc. Convolutional neural network (CNN) [18], one of the most representative deep learning technology, has been applied to reading and analyzing CT images in many recent studies [19], [20]. For example, Koichiro et.…”

Section: Introductionmentioning

confidence: 99%

Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia

Ouyang

Huo

Xia

et al. 2020

IEEE Trans. Med. Imaging

343

268

View full text Add to dashboard Cite

The coronavirus disease (COVID-19) is rapidly spreading all over the world, and has infected more than 1,436,000 people in more than 200 countries and territories as of April 9, 2020. Detecting COVID-19 at early stage is essential to deliver proper healthcare to the patients and also to protect the uninfected population. To this end, we develop a dual-sampling attention network to automatically diagnose COVID-19 from the community acquired pneumonia (CAP) in chest computed tomography (CT). In particular, we propose a novel online attention module with a 3D convolutional network (CNN) to focus on the infection regions in lungs when making decisions of diagnoses. Note that there exists imbalanced distribution of the sizes of the infection regions between COVID-19 and CAP, partially due to fast progress of COVID-19 after symptom onset. Therefore, we develop a dual-sampling strategy to mitigate the imbalanced learning. Our method is evaluated (to our best knowledge) upon the largest multi-center CT data for COVID-19 from 8 hospitals. In the training-validation stage, we collect 2186 CT scans from 1588 patients for a 5-fold cross-validation. In the testing stage, we employ another independent large-scale testing dataset including 2796 CT scans from 2057 patients. Results show that our algorithm can identify the COVID-19 images with the area under the receiver operating characteristic curve (AUC) value of 0.944,

show abstract

“…Convolutional neural networks were introduced as a potential solution to problems faced in automatic organ segmentation [ 57 , 68 ].…”

Section: Resultsmentioning

confidence: 99%

Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review

Adly¹,

Adly²,

Adly³

2020

J Med Internet Res

122

View full text Add to dashboard Cite

Background Artificial intelligence (AI) and the Internet of Intelligent Things (IIoT) are promising technologies to prevent the concerningly rapid spread of coronavirus disease (COVID-19) and to maximize safety during the pandemic. With the exponential increase in the number of COVID-19 patients, it is highly possible that physicians and health care workers will not be able to treat all cases. Thus, computer scientists can contribute to the fight against COVID-19 by introducing more intelligent solutions to achieve rapid control of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes the disease. Objective The objectives of this review were to analyze the current literature, discuss the applicability of reported ideas for using AI to prevent and control COVID-19, and build a comprehensive view of how current systems may be useful in particular areas. This may be of great help to many health care administrators, computer scientists, and policy makers worldwide. Methods We conducted an electronic search of articles in the MEDLINE, Google Scholar, Embase, and Web of Knowledge databases to formulate a comprehensive review that summarizes different categories of the most recently reported AI-based approaches to prevent and control the spread of COVID-19. Results Our search identified the 10 most recent AI approaches that were suggested to provide the best solutions for maximizing safety and preventing the spread of COVID-19. These approaches included detection of suspected cases, large-scale screening, monitoring, interactions with experimental therapies, pneumonia screening, use of the IIoT for data and information gathering and integration, resource allocation, predictions, modeling and simulation, and robotics for medical quarantine. Conclusions We found few or almost no studies regarding the use of AI to examine COVID-19 interactions with experimental therapies, the use of AI for resource allocation to COVID-19 patients, or the use of AI and the IIoT for COVID-19 data and information gathering/integration. Moreover, the adoption of other approaches, including use of AI for COVID-19 prediction, use of AI for COVID-19 modeling and simulation, and use of AI robotics for medical quarantine, should be further emphasized by researchers because these important approaches lack sufficient numbers of studies. Therefore, we recommend that computer scientists focus on these approaches, which are still not being adequately addressed.

show abstract

Automatic Lung Segmentation Based on Texture and Deep Features of HRCT Images with Interstitial Lung Disease

Cited by 36 publications

References 38 publications

Abnormal lung quantification in chest CT images of COVID‐19 patients with deep learning and its application to severity prediction

Abnormal lung quantification in chest CT images of COVID‐19 patients with deep learning and its application to severity prediction

Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia

Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review

Contact Info

Product

Resources

About