Shinohara Toshihiro, Murakami Kosuke, Matsumura Noriomi
Department of Computational Systems Biology, Faculty of Biology-Oriented Science and Technology, Kindai University, Kinokawa 649-6493, Wakayama, Japan.
Department of Obstetrics and Gynecology, Faculty of Medicine, Kindai University, Osakasayama 589-8511, Osaka, Japan.
Diagnostics (Basel). 2023 Apr 29;13(9):1596. doi: 10.3390/diagnostics13091596.
Colposcopy is an essential examination tool to identify cervical intraepithelial neoplasia (CIN), a precancerous lesion of the uterine cervix, and to sample its tissues for histological examination. In colposcopy, gynecologists visually identify the lesion highlighted by applying an acetic acid solution to the cervix using a magnifying glass. This paper proposes a deep learning method to aid the colposcopic diagnosis of CIN by segmenting lesions. In this method, to segment the lesion effectively, the colposcopic images taken before acetic acid solution application were input to the deep learning network, U-Net, for lesion segmentation with the images taken following acetic acid solution application. We conducted experiments using 30 actual colposcopic images of acetowhite epithelium, one of the representative types of CIN. As a result, it was confirmed that accuracy, precision, and F1 scores, which were 0.894, 0.837, and 0.834, respectively, were significantly better when images taken before and after acetic acid solution application were used than when only images taken after acetic acid solution application were used (0.882, 0.823, and 0.823, respectively). This result indicates that the image taken before acetic acid solution application is helpful for accurately segmenting the CIN in deep learning.
阴道镜检查是一种重要的检查工具，用于识别子宫颈上皮内瘤变（CIN），即子宫颈的一种癌前病变，并对其组织进行取样以进行组织学检查。在阴道镜检查中，妇科医生使用放大镜，通过向子宫颈涂抹醋酸溶液来目视识别突出显示的病变。本文提出了一种深度学习方法，通过对病变进行分割来辅助CIN的阴道镜诊断。在该方法中，为了有效地分割病变，将涂抹醋酸溶液之前拍摄的阴道镜图像输入到深度学习网络U-Net中，与涂抹醋酸溶液之后拍摄的图像一起用于病变分割。我们使用30张醋白上皮（CIN的代表性类型之一）的实际阴道镜图像进行了实验。结果证实，当使用涂抹醋酸溶液前后拍摄的图像时，准确率、精确率和F1分数分别为0.894、0.837和0.834，明显优于仅使用涂抹醋酸溶液之后拍摄的图像时的结果（分别为0.882、0.823和0.823）。这一结果表明，涂抹醋酸溶液之前拍摄的图像有助于在深度学习中准确分割CIN。
