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Proceedings Paper

Segmentation of lung region from chest x-ray images using U-net
Author(s): Keigo Furutani; Yasushi Hirano; Shoji Kido
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Paper Abstract

In recent years, many medical image analysis methods based on the Deep Learning techniques have been proposed. The Deep Learning techniques have been used for various medical applications such as organ segmentation and cancer detection. Segmentation of lung region from chest X-ray (CXR) images is also important task for computer-aided diagnosis (CAD). However, many methods based on Deep Learning techniques for this purpose were proposed, the regions where the lung and the heart overlap have been excluded from the target to be extracted in spite of the importance for detection of diseases. The aim of this paper is to extract whole lung regions from CRX images by using the U-net based method. As widely known, the U-net shows its high performance for various applications. As the result of the experiment, the authors archive 0.91 in the average of the Dice coefficient.

Paper Details

Date Published: 27 March 2019
PDF: 5 pages
Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 1105010 (27 March 2019); doi: 10.1117/12.2521594
Show Author Affiliations
Keigo Furutani, Yamaguchi Univ. (Japan)
Yasushi Hirano, Yamaguchi Univ. (Japan)
Shoji Kido, Yamaguchi Univ. (Japan)


Published in SPIE Proceedings Vol. 11050:
International Forum on Medical Imaging in Asia 2019
Feng Lin; Hiroshi Fujita; Jong Hyo Kim, Editor(s)

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