Lung region extraction from CT images of diffuse lung disease cases by deep learning

Daisuke Kurata; Shoji Kido; Yasushi Hirano

doi:10.1117/12.2590660

20 April 2021 Lung region extraction from CT images of diffuse lung disease cases by deep learning

Daisuke Kurata, Shoji Kido, Yasushi Hirano

Author Affiliations +

Proceedings Volume 11792, International Forum on Medical Imaging in Asia 2021; 1179206 (2021) https://doi.org/10.1117/12.2590660
Event: International Forum on Medical Imaging in Asia 2021, 2021, Taipei, Taiwan

Abstract

Diffuse lung diseases (DLD) are widely distributed in lungs. Because the opacity patterns of DLD on CT images are complex and various, the diagnostic results may be different between doctors depending on their experience and subjective decision on them. In order to solve this problem, performing image analysis using CAD (Computer-Aided Diagnosis) systems attracts attention. To achieve high performance in diagnosis by using these CAD systems, it is necessary to first perform lung region extraction as preprocessing for limiting the target domain. However, by using the existing systems, it is difficult to extract lung regions from all five typical shadow patterns of DLD and normal lungs. In this study, we aimed to extract lung regions from CT slices containing DLD shadows using the U-net for improving the CAD performance.

Citation Download Citation

Daisuke Kurata, Shoji Kido, and Yasushi Hirano "Lung region extraction from CT images of diffuse lung disease cases by deep learning", Proc. SPIE 11792, International Forum on Medical Imaging in Asia 2021, 1179206 (20 April 2021); https://doi.org/10.1117/12.2590660

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