Deep learning approach for metastatic cancer cell classification using live-cell imaging data
Deep learning approach for metastatic cancer cell classification using live-cell imaging data
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DOI：
10.1117/12.2608017
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发表时间：
2022-03
期刊：
影响因子：
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通讯作者：
Seohyun Lee;Hyuno Kim;Hideo Higuchi;Masatoshi Ishikawa
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文献类型：
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作者：
Seohyun Lee;Hyuno Kim;Hideo Higuchi;Masatoshi Ishikawa
从致病的角度来看，癌细胞的转移特征被认为是最有问题的特征之一。由于转移性癌细胞通常比非转移性癌细胞表现出更高的迁移率，通过图像区分转移性癌细胞可以为理解细胞转移相关行为的分子过程提供线索。在这项研究中，我们提出了一种深度学习的方法，根据相差显微镜获得的单细胞图像来区分转移癌细胞和非转移癌细胞。
The metastatic profile of the cancer cell is considered to be one of the most problematic characteristics from the pathogenic point of view. Because the metastatic cancer cells often show higher mobility compared to the non-metastatic cancer cells, distinguishing the metastatic cancer cell by their images can contain a clue to understanding the molecular process of the cellular metastasis-associated behaviors. In this study, we suggest a deep-learning approach to classify the metastatic cancer cells and non-metastatic cancer cells by their single-cell images acquired by phase-contrast microscopy.