scholarly journals Deep Learning-Based Identification of Spinal Metastasis in Lung Cancer Using Spectral CT Images

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Xiaojie Fan ◽  
Xiaoyu Zhang ◽  
Zibo Zhang ◽  
Yifang Jiang
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Detection Rate ◽  
Learning Algorithm ◽  
Spinal Metastasis ◽  
Ct Images ◽  
Ct Image ◽  
Spectral Ct ◽  
Deep Learning Algorithm ◽  
Noise Ratio

In this study, deep learning algorithm-based energy/spectral computed tomography (CT) for the spinal metastasis from lung cancer was used. A dilated convolutional U-Net model (DC-U-Net model) was first proposed, which was used to segment the energy/spectral CT image of patients with the spinal metastasis from lung cancer. Subsequently, energy/spectral CT images under different energy levels were collected for the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) comparison. It was found the learning rate of the model decreased exponentially as the number of training increased, with the lung contour segmented out of the image. Under 40–65 keV, the CT value of bone metastasis from lung cancer decreased with increasing energy, as with the average rank sum test result. The SNR and CNR values were the highest under 60 keV. The detection rate of the deep learning algorithm below 60 keV was 81.41%, and that of professional doctors was 77.56%. The detection rate of the deep learning algorithm below 140 keV was 66.03%, and that of professional doctors was 64.74%. In conclusion, the DC-U-Net model demonstrates better segmentation effects versus the convolutional neutral networ k (CNN), with the lung contour segmented. Further, a higher energy level leads to worse segmentation effects on the energy/spectral CT image.

Potential value of the PixelShine deep learning algorithm for increasing quality of 70 kVp+ASiR-V reconstruction pelvic arterial phase CT images

2018 ◽  
Vol 37 (2) ◽  
pp. 186-190 ◽  
Author(s):  
Shi-feng Tian ◽  
Ai-lian Liu ◽  
Jing-hong Liu ◽  
Yi-jun Liu ◽  
Ju-dong Pan
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Ct Images ◽  
Arterial Phase ◽  
Potential Value ◽  
Deep Learning Algorithm

scholarly journals Evaluation of Glucocorticoid Therapy in Asthma Children with Small Airway Obstruction Based on CT Features of Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Wei Zhang ◽  
Yang Wang
Keyword(s):  
Deep Learning ◽  
Airway Obstruction ◽  
Airway Inflammation ◽  
Maintenance Treatment ◽  
Learning Algorithm ◽  
Small Airway ◽  
Imaging Features ◽  
Ct Image ◽  
Deep Learning Algorithm ◽  
Small Airway Obstruction

This study was aimed at exploring the treatment of asthma children with small airway obstruction in CT imaging features of deep learning and glucocorticoid. A total of 145 patients meeting the requirements in hospital were included in this study, and they were randomly assigned to receive aerosolized glucocorticoid ( n = 45 ), aerosolized glucocorticoid combined with bronchodilator ( n = 50 ), or oral steroids ( n = 50 ) for 4 weeks after discharge. The lung function and fractional exhaled nitric oxide (FENO) indexes of the three groups were measured, respectively, and then the effective rates were compared to evaluate the clinical efficacy of glucocorticoids with different administration methods and combined medications in the short-term maintenance treatment after acute exacerbation of asthma. Deep learning algorithm was used for CT image segmentation. The CT image is sent to the workbench for processing on the workbench, and then the convolution operation is performed on each input pixel point during the image processing. After 4 weeks of maintenance treatment, FEF50 %, FEF75 %, and MMEF75/25 increased significantly, and FENO decreased significantly ( P < 0.01 ). The improvement results of FEF50 %, FEF75 %, MMEF75/25, and FENO after maintenance treatment were as follows: the oral hormone group was the most effective, followed by the combined atomization inhalation group, and the hormone atomization inhalation group was the least effective. The differences among them were statistically significant ( P < 0.05 ). The accuracy of artificial intelligence segmentation algorithm was 81%. All the hormones were more effective than local medication in the treatment of small airway function and airway inflammation. In the treatment of aerosol inhalation, the hormone combined with bronchiectasis drug was the most effective in improving small airway obstruction and reducing airway inflammation compared with single drug inhalation. Deep learning CT images are simple, noninvasive, and intuitively observe lung changes in asthma with small airway functional obstruction. Asthma with small airway functional obstruction has high clinical diagnosis and evaluation value.

scholarly journals Deep Learning Based Automated Orthotopic Lung Tumor Segmentation in Whole-Body Mouse CT-Scans

Cancers ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 4585
Author(s):  
Wouter R. P. H. van de Worp ◽  
Brent van der Heyden ◽  
Georgios Lappas ◽  
Ardy van Helvoort ◽  
Jan Theys ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Tumor Growth ◽  
Tumor Volume ◽  
Learning Algorithm ◽  
Lung Tumor ◽  
Whole Body ◽  
Ct Scans ◽  
Therapeutic Effects ◽  
Deep Learning Algorithm

Lung cancer is the leading cause of cancer related deaths worldwide. The development of orthotopic mouse models of lung cancer, which recapitulates the disease more realistically compared to the widely used subcutaneous tumor models, is expected to critically aid the development of novel therapies to battle lung cancer or related comorbidities such as cachexia. However, follow-up of tumor take, tumor growth and detection of therapeutic effects is difficult, time consuming and requires a vast number of animals in orthotopic models. Here, we describe a solution for the fully automatic segmentation and quantification of orthotopic lung tumor volume and mass in whole-body mouse computed tomography (CT) scans. The goal is to drastically enhance the efficiency of the research process by replacing time-consuming manual procedures with fast, automated ones. A deep learning algorithm was trained on 60 unique manually delineated lung tumors and evaluated by four-fold cross validation. Quantitative performance metrics demonstrated high accuracy and robustness of the deep learning algorithm for automated tumor volume analyses (mean dice similarity coefficient of 0.80), and superior processing time (69 times faster) compared to manual segmentation. Moreover, manual delineations of the tumor volume by three independent annotators was sensitive to bias in human interpretation while the algorithm was less vulnerable to bias. In addition, we showed that besides longitudinal quantification of tumor development, the deep learning algorithm can also be used in parallel with the previously published method for muscle mass quantification and to optimize the experimental design reducing the number of animals needed in preclinical studies. In conclusion, we implemented a method for fast and highly accurate tumor quantification with minimal operator involvement in data analysis. This deep learning algorithm provides a helpful tool for the noninvasive detection and analysis of tumor take, tumor growth and therapeutic effects in mouse orthotopic lung cancer models.

scholarly journals Development and Evaluation of a Deep Learning Algorithm for Rib Segmentation and Fracture Detection from Multi-Center Chest CT Images

2021 ◽  
pp. e200248
Author(s):  
Mingxiang Wu ◽  
Zhizhong Chai ◽  
Guangwu Qian ◽  
Huangjing Lin ◽  
Qiong Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Ct Images ◽  
Chest Ct ◽  
Fracture Detection ◽  
Deep Learning Algorithm

scholarly journals Enhanced Lung Cancer Detection using Deep Learning Algorithm

2020 ◽  
Vol 9 (7) ◽  
pp. 1016-1020
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Cancer Detection ◽  
Environmental Conditions ◽  
Learning Algorithm ◽  
The Other ◽  
Deep Learning Algorithm ◽  
The People ◽  
Sensitivity Specificity ◽  
Lung Cancer Detection

Lung cancer is more dangerous than any other cancer. Nowadays many people are affecting lung cancer because of their lifestyle and environmental conditions. The basic cause of lung cancer is smoking. Many steps are taken to avoid smoking but on the other way the cancer is affecting the people. In this paper, the Enhanced Deep Learning (EDL) based algorithm is introduced to detects cancer in lungs in various patients based on their symptoms. It is very important to detect the cancer in the earliers stages. The proposed system calculates the three parameters such as sensitivity, specificity and accuracy. Results show the performance of the proposed system.

scholarly journals Deep Learning Algorithm for Reducing CT Slice Thickness: Effect on Reproducibility of Radiomic Features in Lung Cancer

2019 ◽  
Vol 20 (10) ◽  
pp. 1431 ◽  
Author(s):  
Sohee Park ◽  
Sang Min Lee ◽  
Kyung-Hyun Do ◽  
June-Goo Lee ◽  
Woong Bae ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Thickness Effect ◽  
Slice Thickness ◽  
Deep Learning Algorithm

scholarly journals A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)

2021 ◽  
Author(s):  
Shuai Wang ◽  
Bo Kang ◽  
Jinlu Ma ◽  
Xianjun Zeng ◽  
Mingming Xiao ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Virus Disease ◽  
Ct Images ◽  
Viral Pneumonia ◽  
Diagnostic Methods ◽  
Deep Learning Algorithm ◽  
Corona Virus ◽  
Total Accuracy

Abstract Objective The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) has caused more than 26 million cases of Corona virus disease (COVID-19) in the world so far. To control the spread of the disease, screening large numbers of suspected cases for appropriate quarantine and treatment are a priority. Pathogenic laboratory testing is typically the gold standard, but it bears the burden of significant false negativity, adding to the urgent need of alternative diagnostic methods to combat the disease. Based on COVID-19 radiographic changes in CT images, this study hypothesized that artificial intelligence methods might be able to extract specific graphical features of COVID-19 and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control. Methods We collected 1065 CT images of pathogen-confirmed COVID-19 cases along with those previously diagnosed with typical viral pneumonia. We modified the inception transfer-learning model to establish the algorithm, followed by internal and external validation. Results The internal validation achieved a total accuracy of 89.5% with a specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with a specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images, the first two nucleic acid test results were negative, and 46 were predicted as COVID-19 positive by the algorithm, with an accuracy of 85.2%. Conclusion These results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis. Key Points • The study evaluated the diagnostic performance of a deep learning algorithm using CT images to screen for COVID-19 during the influenza season. • As a screening method, our model achieved a relatively high sensitivity on internal and external CT image datasets. • The model was used to distinguish between COVID-19 and other typical viral pneumonia, both of which have quite similar radiologic characteristics.

scholarly journals Deep Learning Algorithm for Simultaneous Noise Reduction and Edge Sharpening in Low-Dose CT Images: A Pilot Study Using Lumbar Spine CT

2021 ◽  
Vol 22 ◽  
Author(s):  
Hyunjung Yeoh ◽  
Sung Hwan Hong ◽  
Chulkyun Ahn ◽  
Ja-Young Choi ◽  
Hee-Dong Chae ◽  
...  
Keyword(s):  
Deep Learning ◽  
Lumbar Spine ◽  
Pilot Study ◽  
Noise Reduction ◽  
Low Dose ◽  
Learning Algorithm ◽  
Ct Images ◽  
Low Dose Ct ◽  
Deep Learning Algorithm ◽  
Spine Ct
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