Combining the Gabor and histogram features for classifying diffuse lung opacities in thin-section computed tomography

2003 ◽  
Author(s):  
Y. Mitani ◽  
H. Yasuda ◽  
S. Kido ◽  
K. Ueda ◽  
N. Matsunaga ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Thin Section ◽  
Diffuse Lung

A Rare Case of Kikuchi-Fujimoto Disease With Diffuse Lung Involvement Presenting a Lymphatic-like Distribution on Thin-section Computed Tomography

2018 ◽  
Vol 33 (6) ◽  
pp. W51-W53
Author(s):  
Ryoko Egashira ◽  
Takahiko Nakazono ◽  
Ken Yamaguchi ◽  
Keita Kai ◽  
Nobuyuki Ono ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Thin Section ◽  
Rare Case ◽  
Lung Involvement ◽  
Diffuse Lung

scholarly journals Thin-section Computed Tomography Detects Long-term Pulmonary Sequelae 3 Years after Novel Influenza A Virus-associated Pneumonia

2015 ◽  
Vol 128 (7) ◽  
pp. 902-908 ◽  
Author(s):  
Zhi-Heng Xing ◽  
Xin Sun ◽  
Long Xu ◽  
Qi Wu ◽  
Li Li ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Influenza A Virus ◽  
Thin Section ◽  
Influenza A ◽  
Novel Influenza A

Development and evaluation of a computer-based decision support system for diffuse lung diseases at high-resolution computed tomography

2021 ◽  
pp. 028418512199579
Author(s):  
Simon S Martin ◽  
Delina Kolaneci ◽  
Julian L Wichmann ◽  
Lukas Lenga ◽  
Doris Leithner ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Decision Support ◽  
High Resolution ◽  
Lung Diseases ◽  
Differential Diagnoses ◽  
High Resolution Computed Tomography ◽  
Computer Based ◽  
Diffuse Lung Diseases ◽  
Hrct Patterns ◽  
Diffuse Lung

Background High-resolution computed tomography (HRCT) is essential in narrowing the possible differential diagnoses of diffuse and interstitial lung diseases. Purpose To investigate the value of a novel computer-based decision support system (CDSS) for facilitating diagnosis of diffuse lung diseases at HRCT. Material and Methods A CDSS was developed that includes about 100 different illustrations of the most common HRCT signs and patterns and describes the corresponding pathologies in detail. The logical set-up of the software facilitates a structured evaluation. By selecting one or more CT patterns, the program generates a ranked list of the most likely differential diagnoses. Three independent and blinded radiology residents initially evaluated 40 cases with different lung diseases alone; after at least 12 weeks, observers re-evaluated all cases using the CDSS. Results In 40 patients, a total of 113 HRCT patterns were evaluated. The percentage of correctly classified patterns was higher with CDSS (96.8%) compared to assessment without CDSS (90.3%; P < 0.01). Moreover, the percentage of correct diagnosis (81.7% vs. 64.2%) and differential diagnoses (89.2% vs. 38.3%) were superior with CDSS compared to evaluation without CDSS (both P < 0.01). Conclusion Addition of a CDSS using a structured approach providing explanations of typical HRCT patterns and graphical illustrations significantly improved the performance of trainees in characterizing and correctly identifying diffuse lung diseases.

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