Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:3982-3985. doi: 10.1109/EMBC46164.2021.9630443.
Histopathological images are widely used to diagnose diseases such as skin cancer. As digital histopathological images are typically of very large size, in the order of several billion pixels, automated identification of abnormal cell nuclei and their distribution within multiple tissue sections would enable rapid comprehensive diagnostic assessment. In this paper, we propose a deep learning-based technique to segment the melanoma regions in Hematoxylin and Eosin-stained histopathological images. In this technique, the nuclei in an image are first segmented using a deep learning neural network. The segmented nuclei are then used to generate the melanoma region masks. Experimental results show that the proposed method can provide nuclei segmentation accuracy of around 90% and the melanoma region segmentation accuracy of around 98%. The proposed technique also has a low computational complexity.
组织病理学图像被广泛用于诊断皮肤癌等疾病。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，因此自动识别异常细胞核及其在多个组织切片中的分布，可以实现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，首先使用深度学习神经网络对图像中的细胞核进行分割。然后，使用分割后的细胞核生成黑色素瘤区域掩模。实验结果表明，所提出的方法可以提供约 90%的细胞核分割精度和约 98%的黑色素瘤区域分割精度。该技术的计算复杂度也较低。
