| アイテムタイプ |
学術雑誌論文 = Journal Article(1) |
| 公開日 |
2023-10-10 |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| タイトル |
|
|
タイトル |
Automatic Identification of Tumor Cells for Circulating Tumor Cells by Convolutional Neural Networks |
|
言語 |
en |
| その他のタイトル |
|
|
その他のタイトル |
AUTOMATIC IDENTIFICATION OF TUMOR CELLS FOR CIRCULATING TUMOR CELLS BY CONVOLUTIONAL NEURAL NETWORKS |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| 著者 |
Hashimoto, Kazuki
神谷, 亨
Li, Guangxu
Yoneda, Kazue
Tanaka, Fumihiro
|
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
Liquid biopsy allows non-invasive collection of circulating tumor cells (CTCs) in blood without the need for sampling and can clearly demonstrate their presence in many types of cancer. In this study, we propose a method to automatically identify CTCs from fluorescence microscopy images and enable quantitative analysis based on convolutional neural networks (CNN). In this paper, a cell nucleus region cropping algorithm is applied in addition to a filtering process centered on a selective enhancement filter. Next, identification by SqueezeNet is performed. We performed the proposed method to 5,040 images of 6 samples and conducted experiments to identify CTCs. The number of detected CTCs was 148 (TPR = 100%), and the number of over-detected non-CTCs was 925. For the identification, TPR = 88.51% and FPR = 5.102% for the CNN model using SqueezeNet. The proposed method successfully reduced the number of detections by about 71.4% without missing any correct answers, but the proposed method did not show good results in any of the evaluation metrics. |
|
言語 |
en |
| 書誌情報 |
en : International Journal of Innovative Computing, Information and Control
巻 19,
号 1,
発行日 2023-02
|
| 出版社 |
|
|
出版者 |
ICIC International |
|
言語 |
en |
| DOI |
|
|
関連タイプ |
isIdenticalTo |
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.24507/ijicic.19.01.1 |
| ISSN |
|
|
収録物識別子タイプ |
PISSN |
|
収録物識別子 |
1349-4198 |
| 著作権関連情報 |
|
|
権利情報 |
ICIC International Copyright (c) 2023 |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
Circulating tumor cells |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
Computer aided diagnosis |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
Cell nucleus |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
Convolutional neural network |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
SqueezeNet |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| 査読の有無 |
|
|
値 |
yes |
LaSEINE-2022_026.pdf (1.3 MB)
