2018年12月10日
Automatic extraction of abnormalities on temporal CT subtraction images using sparse coding and 3D-CNn
International Conference on Control, Automation and Systems
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- 巻
- 2018-October
- 号
- 開始ページ
- 1468
- 終了ページ
- 1471
- 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)
© ICROS. In recent years, the proportion of deaths from cancer tends to increase in Japan, especially the number of deaths from lung cancer is increasing. CT device is effective for early detection of lung cancer. However, there is concern that an increase in burden on doctors will be caused by high performance of CT improving. Therefore, by presenting the “second opinion” by the CAD system, it reduces the burden on the doctor. In this paper, we develop a CAD system for automatic detection of lesion candidate regions such as lung nodules or ground glass opacity (GGO) from 3D CT images. Our proposed method consists of three steps. In the first step, lesion candidate regions are extracted using temporal subtraction technique. In the second step, the image is reconstructed by sparse coding for the extracted region. In the final step, 3D Convolutional Neural Network (3D-CNN) identification using reconstructed images is performed. We applied our method to 51 cases and True Positive rate (TP) of 79.81 % and False Positive rate (FP) of 37.65 % are obtained.
- リンク情報
- ID情報
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- ISSN : 1598-7833
- SCOPUS ID : 85060466308