scholarly journals Esophagus Segmentation in Computed Tomography Images Using a U-Net Neural Network With a Semiautomatic Labeling Method

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 202459-202468
Author(s):  
Xiao Lou ◽  
Youzhe Zhu ◽  
Kumaradevan Punithakumar ◽  
Lawrence H. Le ◽  
Baosheng Li
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Computed Tomography Images ◽  
Labeling Method

scholarly journals Metal artifact reduction in computed tomography images based on developed generative adversarial neural network

2021 ◽  
Vol 24 ◽  
pp. 100573
Author(s):  
Goli Khaleghi ◽  
Mohammad Hosntalab ◽  
Mahdi Sadeghi ◽  
Reza Reiazi ◽  
Seied Rabi Mahdavi
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Computed Tomography Images

An artificial intelligence algorithm that identifies middle turbinate pneumatisation (concha bullosa) on sinus computed tomography scans

2020 ◽  
Vol 134 (4) ◽  
pp. 328-331 ◽  
Author(s):  
P Parmar ◽  
A-R Habib ◽  
D Mendis ◽  
A Daniel ◽  
M Duvnjak ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Computed Tomography ◽  
Diagnostic Accuracy ◽  
Convolutional Neural Network ◽  
Middle Turbinate ◽  
Concha Bullosa ◽  
Computed Tomography Images ◽  
Computed Tomography Scans ◽  
Neural Network Algorithm

AbstractObjectiveConvolutional neural networks are a subclass of deep learning or artificial intelligence that are predominantly used for image analysis and classification. This proof-of-concept study attempts to train a convolutional neural network algorithm that can reliably determine if the middle turbinate is pneumatised (concha bullosa) on coronal sinus computed tomography images.MethodConsecutive high-resolution computed tomography scans of the paranasal sinuses were retrospectively collected between January 2016 and December 2018 at a tertiary rhinology hospital in Australia. The classification layer of Inception-V3 was retrained in Python using a transfer learning method to interpret the computed tomography images. Segmentation analysis was also performed in an attempt to increase diagnostic accuracy.ResultsThe trained convolutional neural network was found to have diagnostic accuracy of 81 per cent (95 per cent confidence interval: 73.0–89.0 per cent) with an area under the curve of 0.93.ConclusionA trained convolutional neural network algorithm appears to successfully identify pneumatisation of the middle turbinate with high accuracy. Further studies can be pursued to test its ability in other clinically important anatomical variants in otolaryngology and rhinology.

scholarly journals Kidney segmentation from computed tomography images using deep neural network

2020 ◽  
Vol 123 ◽  
pp. 103906 ◽  
Author(s):  
Luana Batista da Cruz ◽  
José Denes Lima Araújo ◽  
Jonnison Lima Ferreira ◽  
João Otávio Bandeira Diniz ◽  
Aristófanes Corrêa Silva ◽  
...  
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Deep Neural Network ◽  
Computed Tomography Images ◽  
Kidney Segmentation
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