PDF(Pubmed)
Abstract：
UNASSIGNED: Accurate identification of epidermal cells on reflectance confocal microscopy (RCM) images is important in the study of epidermal architecture and topology of both healthy and diseased skin. However, analysis of these images is currently done manually and therefore time-consuming and subject to human error and inter-expert interpretation. It is also hindered by low image quality due to noise and heterogeneity.
UNASSIGNED: We aimed to design an automated pipeline for the analysis of the epidermal structure from RCM images.
UNASSIGNED: Two attempts have been made at automatically localizing epidermal cells, called keratinocytes, on RCM images: the first is based on a rotationally symmetric error function mask, and the second on cell morphological features. Here, we propose a dual-task network to automatically identify keratinocytes on RCM images. Each task consists of a cycle generative adversarial network. The first task aims to translate real RCM images into binary images, thus learning the noise and texture model of RCM images, whereas the second task maps Gabor-filtered RCM images into binary images, learning the epidermal structure visible on RCM images. The combination of the two tasks allows one task to constrict the solution space of the other, thus improving overall results. We refine our cell identification by applying the pre-trained StarDist algorithm to detect star-convex shapes, thus closing any incomplete membranes and separating neighboring cells.
UNASSIGNED: The results are evaluated both on simulated data and manually annotated real RCM data. Accuracy is measured using recall and precision metrics, which is summarized as the F 1 -score.
UNASSIGNED: We demonstrate that the proposed fully unsupervised method successfully identifies keratinocytes on RCM images of the epidermis, with an accuracy on par with experts\' cell identification, is not constrained by limited available annotated data, and can be extended to images acquired using various imaging techniques without retraining.
UNASSIGNED: We aimed to design an automated pipeline for the analysis of the epidermal structure from RCM images.
UNASSIGNED: Two attempts have been made at automatically localizing epidermal cells, called keratinocytes, on RCM images: the first is based on a rotationally symmetric error function mask, and the second on cell morphological features. Here, we propose a dual-task network to automatically identify keratinocytes on RCM images. Each task consists of a cycle generative adversarial network. The first task aims to translate real RCM images into binary images, thus learning the noise and texture model of RCM images, whereas the second task maps Gabor-filtered RCM images into binary images, learning the epidermal structure visible on RCM images. The combination of the two tasks allows one task to constrict the solution space of the other, thus improving overall results. We refine our cell identification by applying the pre-trained StarDist algorithm to detect star-convex shapes, thus closing any incomplete membranes and separating neighboring cells.
UNASSIGNED: The results are evaluated both on simulated data and manually annotated real RCM data. Accuracy is measured using recall and precision metrics, which is summarized as the F 1 -score.
UNASSIGNED: We demonstrate that the proposed fully unsupervised method successfully identifies keratinocytes on RCM images of the epidermis, with an accuracy on par with experts\' cell identification, is not constrained by limited available annotated data, and can be extended to images acquired using various imaging techniques without retraining.
摘要：
■在反射共聚焦显微镜（RCM）图像上准确识别表皮细胞对于研究健康和患病皮肤的表皮结构和拓扑结构非常重要。然而,这些图像的分析目前手动完成，因此耗时，并受到人为错误和专家之间的解释。由于噪声和异质性，它还受到低图像质量的阻碍。
■我们旨在设计一种自动化管道，用于从RCM图像分析表皮结构。
■已经进行了两种自动定位表皮细胞的尝试，称为角质形成细胞，在RCM图像上：第一个是基于旋转对称误差函数掩码，第二个是细胞形态特征。这里,我们提出了一个双任务网络来自动识别RCM图像上的角质形成细胞。每个任务由一个循环生成对抗网络组成。第一项任务旨在将真实的RCM图像转换为二进制图像，从而学习RCM图像的噪声和纹理模型，而第二个任务将Gabor过滤的RCM图像映射为二进制图像，学习RCM图像上可见的表皮结构。两个任务的组合允许一个任务限制另一个任务的解空间，从而提高整体效果。我们通过应用预先训练的StarDist算法来检测星凸形状来完善我们的细胞识别，从而关闭任何不完整的膜并分离相邻的细胞。
■在模拟数据和手动注释的真实RCM数据上评估结果。准确性是使用召回率和精确度指标来衡量的，总结为F1分数。
■我们证明了所提出的完全无监督的方法成功地识别了表皮RCM图像上的角质形成细胞，准确性与专家的细胞识别相当，不受有限的可用注释数据的约束，并且可以扩展到使用各种成像技术获取的图像，而无需重新训练。
■我们旨在设计一种自动化管道，用于从RCM图像分析表皮结构。
■已经进行了两种自动定位表皮细胞的尝试，称为角质形成细胞，在RCM图像上：第一个是基于旋转对称误差函数掩码，第二个是细胞形态特征。这里,我们提出了一个双任务网络来自动识别RCM图像上的角质形成细胞。每个任务由一个循环生成对抗网络组成。第一项任务旨在将真实的RCM图像转换为二进制图像，从而学习RCM图像的噪声和纹理模型，而第二个任务将Gabor过滤的RCM图像映射为二进制图像，学习RCM图像上可见的表皮结构。两个任务的组合允许一个任务限制另一个任务的解空间，从而提高整体效果。我们通过应用预先训练的StarDist算法来检测星凸形状来完善我们的细胞识别，从而关闭任何不完整的膜并分离相邻的细胞。
■在模拟数据和手动注释的真实RCM数据上评估结果。准确性是使用召回率和精确度指标来衡量的，总结为F1分数。
■我们证明了所提出的完全无监督的方法成功地识别了表皮RCM图像上的角质形成细胞，准确性与专家的细胞识别相当，不受有限的可用注释数据的约束，并且可以扩展到使用各种成像技术获取的图像，而无需重新训练。
