論文

査読有り
2019年

Segmentation of lung region from chest x-ray images using U-net

Proceedings of SPIE - The International Society for Optical Engineering
  • Keigo Furutani
  • ,
  • Yasushi Hirano
  • ,
  • Shoji Kido

11050
記述言語
掲載種別
研究論文(国際会議プロシーディングス)
DOI
10.1117/12.2521594

© 2019 SPIE. 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.


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DOI
https://doi.org/10.1117/12.2521594
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