Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada.
Department of Medicine, University of Alberta, Edmonton, AB, Canada.
Tissue Cell. 2021 Dec;73:101659. doi: 10.1016/j.tice.2021.101659. Epub 2021 Sep 29.
Histopathological images are widely used to diagnose diseases including skin cancer. As digital histopathological images are typically of very large size, in the order of several billion pixels, automated identification of all abnormal cell nuclei and their distribution within multiple tissue sections would assist rapid comprehensive diagnostic assessment. In this paper, we propose a deep learning-based technique to segment the melanoma regions in Hematoxylin and Eosin (H&E) stained histopathological images. In this technique, the nuclei in the image are first segmented using a Convolutional Neural Network (CNN). The segmented nuclei are then used to generate melanoma region masks. Experimental results with a small melanoma dataset show that the proposed method can potentially segment the nuclei with more than 94 % accuracy and segment the melanoma regions with a Dice coefficient of around 85 %. The proposed technique also has a small execution time making it suitable for clinical diagnosis with a fast turnaround time.
组织病理学图像被广泛用于诊断疾病，包括皮肤癌。由于数字组织病理学图像通常非常大，像素数量达到数十亿，因此自动识别所有异常细胞核及其在多个组织切片中的分布将有助于快速全面的诊断评估。在本文中，我们提出了一种基于深度学习的技术，用于分割苏木精和伊红（H&E）染色组织病理学图像中的黑色素瘤区域。在该技术中，首先使用卷积神经网络（CNN）分割图像中的细胞核。然后，使用分割后的细胞核生成黑色素瘤区域掩模。使用小型黑色素瘤数据集进行的实验结果表明，该方法有可能以超过 94%的准确率分割细胞核，并以约 85%的骰子系数分割黑色素瘤区域。该技术的执行时间也很短，非常适合具有快速周转时间的临床诊断。
