https://scholars.lib.ntu.edu.tw/handle/123456789/633073
標題: | H&E-like staining of OCT images of human skin via generative adversarial network | 作者: | Tsai, Sheng Ting Liu, Chih Hao Chan, Chin Cheng Li, Yi Hsin SHENG-LUNG HUANG HOMER H. CHEN |
公開日期: | 26-九月-2022 | 卷: | 121 | 期: | 13 | 來源出版物: | Applied Physics Letters | 摘要: | Noninvasive and high-speed optical coherence tomography (OCT) systems have been widely deployed for daily clinical uses. High-resolution OCTs are advancing rapidly; however, grey-level OCT images are not easy to read for pathologists due to the lack of diagnosis specificity compared with hematoxylin and eosin (H&E) stained images. This work presents an OCT to H&E image translation model to convert the OCT images to H&E-like stained images using unpaired OCT and H&E datasets. "H&E like"means the stratum corneum (SC) boundary and the dermal-epidermal junction (DEJ) of the OCT and the translated images are consistent. Pre-trained segmentation models for the DEJ and the SC are exploited to enhance the performance of anatomical image translation and reduce the DEJ and SC lower boundary errors to ±2.3 and ±1.7 μm, respectively. A pre-trained VGG16 network extracts the features of the nuclei. Pearson's correlation coefficient of the nuclei location and size consistency is 84% ± 1%. As a result, in vivo medical image translation accuracy with cellular resolution was achieved. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/633073 | ISSN: | 00036951 | DOI: | 10.1063/5.0122965 |
顯示於: | 電機工程學系 |
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