https://scholars.lib.ntu.edu.tw/handle/123456789/631234
標題: | Deep-learning based breast cancer detection for cross-staining histopathology images | 作者: | Huang, Pei-Wen Ouyang, Hsu Hsu, Bang-Yi Chang, Yu-Ruei Lin, Yu-Chieh Chen, Yung-An Hsieh, Yu-Han Fu, Chien-Chung Li, Chien-Feng CHING-HUNG LIN Lin, Yen-Yin Dah-Tsyr Chang, Margaret Pai, Tun-Wen |
關鍵字: | Adaptive color segmentation; Artificial intelligence; Breast cancer; Color deconvolution; Computational pathology; Cross-staining | 公開日期: | 二月-2023 | 出版社: | ELSEVIER SCI LTD | 卷: | 9 | 期: | 2 | 來源出版物: | Heliyon | 摘要: | Hematoxylin and eosin (H&E) staining is the gold standard for tissue characterization in routine pathological diagnoses. However, these visible light dyes do not exclusively label the nuclei and cytoplasm, making clear-cut segmentation of staining signals challenging. Currently, fluorescent staining technology is much more common in clinical research for analyzing tissue morphology and protein distribution owing to its advantages of channel independence, multiplex labeling, and the possibility of enabling 3D tissue labeling. Although both H&E and fluorescent dyes can stain the nucleus and cytoplasm for representative tissue morphology, color variation between these two staining technologies makes cross-analysis difficult, especially with computer-assisted artificial intelligence (AI) algorithms. In this study, we applied color normalization and nucleus extraction methods to overcome the variation between staining technologies. We also developed an available workflow for using an H&E-stained segmentation AI model in the analysis of fluorescent nucleic acid staining images in breast cancer tumor recognition, resulting in 89.6% and 80.5% accuracy in recognizing specific tumor features in H&E- and fluorescent-stained pathological images, respectively. The results show that the cross-staining inference maintained the same precision level as the proposed workflow, providing an opportunity for an expansion of the application of current pathology AI models. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/631234 | ISSN: | 2405-8440 | DOI: | 10.1016/j.heliyon.2023.e13171 |
顯示於: | 醫學院附設癌醫中心醫院(臺大癌醫) |
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