School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, People's Republic of China.
Phys Med Biol. 2019 Sep 11;64(18):185003. doi: 10.1088/1361-6560/ab0a90.
Cell nuclei image segmentation technology can help researchers observe each cell's stress response to drug treatment. However, it is still a challenge to accurately segment the adherent cell nuclei. At present, image segmentation based on a fully convolutional network (FCN) is attracting researchers' attention. We propose a multiple FCN architecture and repetitive training (M-FCN-RT) method to learn features of cell nucleus images. In M-FCN-RT, the multiple FCN (M-FCN) architecture is composed of several single FCNs (S-FCNs) with the same structure, and each FCN is used to learn the specific features of image datasets. In this paper, the M-FCN contains three FCNs; FCN, FCN and FCN. FCN and FCN are respectively used to learn the spatial features of cell nuclei for generating probability maps to indicate nucleus regions of an image; FCN (boundary FCN) is used to learn the edge features of cell nuclei for generating the nucleus boundary. For the training of each FCN, we propose a repetitive training (RT) method to improve the classification accuracy of the model. To segment cell nuclei, we finally propose an algorithm combining the probability map and boundary (PMB) to segment the adherent nuclei. This paper uses a public opening nucleus image dataset to train, verify and evaluate the proposed M-FCN-RT and PMB methods. Our M-FCN-RT method achieves a high Dice similarity coefficient (DSC) of 92.11%, 95.64% and 87.99% on the three types of sub-datasets respectively for probability maps. In addition, segmentation experimental results show the PMB method is more effective and efficient compared with other methods.
细胞核图像分割技术可以帮助研究人员观察每个细胞对药物治疗的应激反应。然而，准确分割贴壁细胞核仍然是一个挑战。目前，基于全卷积网络（FCN）的图像分割技术引起了研究人员的关注。我们提出了一种多 FCN 架构和重复训练（M-FCN-RT）方法来学习细胞核图像的特征。在 M-FCN-RT 中，多 FCN（M-FCN）架构由几个具有相同结构的单 FCN（S-FCN）组成，每个 FCN 用于学习特定的图像数据集的特征。在本文中，M-FCN 包含三个 FCN，即 FCN、FCN 和 FCN。FCN 和 FCN 分别用于学习细胞核的空间特征，以生成概率图来指示图像的细胞核区域；FCN（边界 FCN）用于学习细胞核的边缘特征，以生成细胞核边界。对于每个 FCN 的训练，我们提出了一种重复训练（RT）方法来提高模型的分类精度。为了分割细胞核，我们最后提出了一种结合概率图和边界的算法（PMB）来分割贴壁核。本文使用一个公开的细胞核图像数据集进行训练、验证和评估所提出的 M-FCN-RT 和 PMB 方法。我们的 M-FCN-RT 方法在三个子数据集的概率图上分别实现了 92.11%、95.64%和 87.99%的高 Dice 相似系数（DSC）。此外，分割实验结果表明，与其他方法相比，PMB 方法更有效和高效。
