Shenzhen Key Laboratory of Intelligent Bioinformatics and Center for High Performance Computing, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Int J Mol Sci. 2023 Nov 7;24(22):16028. doi: 10.3390/ijms242216028.
The automatic detection of cells in microscopy image sequences is a significant task in biomedical research. However, routine microscopy images with cells, which are taken during the process whereby constant division and differentiation occur, are notoriously difficult to detect due to changes in their appearance and number. Recently, convolutional neural network (CNN)-based methods have made significant progress in cell detection and tracking. However, these approaches require many manually annotated data for fully supervised training, which is time-consuming and often requires professional researchers. To alleviate such tiresome and labor-intensive costs, we propose a novel weakly supervised learning cell detection and tracking framework that trains the deep neural network using incomplete initial labels. Our approach uses incomplete cell markers obtained from fluorescent images for initial training on the Induced Pluripotent Stem (iPS) cell dataset, which is rarely studied for cell detection and tracking. During training, the incomplete initial labels were updated iteratively by combining detection and tracking results to obtain a model with better robustness. Our method was evaluated using two fields of the iPS cell dataset, along with the cell detection accuracy (DET) evaluation metric from the Cell Tracking Challenge (CTC) initiative, and it achieved 0.862 and 0.924 DET, respectively. The transferability of the developed model was tested using the public dataset FluoN2DH-GOWT1, which was taken from CTC; this contains two datasets with reference annotations. We randomly removed parts of the annotations in each labeled data to simulate the initial annotations on the public dataset. After training the model on the two datasets, with labels that comprise 10% cell markers, the DET improved from 0.130 to 0.903 and 0.116 to 0.877. When trained with labels that comprise 60% cell markers, the performance was better than the model trained using the supervised learning method. This outcome indicates that the model's performance improved as the quality of the labels used for training increased.
显微镜图像序列中细胞的自动检测是生物医学研究中的一项重要任务。然而，由于细胞外观和数量的变化，在细胞不断分裂和分化的过程中拍摄的常规显微镜图像非常难以检测。最近，基于卷积神经网络（CNN）的方法在细胞检测和跟踪方面取得了重大进展。然而，这些方法需要大量手动标注的数据进行完全监督训练，这既耗时又需要专业研究人员。为了减轻这种繁琐和劳动密集型的成本，我们提出了一种新颖的弱监督学习细胞检测和跟踪框架，该框架使用不完整的初始标签来训练深度神经网络。我们的方法使用从荧光图像中获得的不完整细胞标记物来对诱导多能干细胞（iPS）细胞数据集进行初始训练，该数据集很少用于细胞检测和跟踪研究。在训练过程中，通过结合检测和跟踪结果，迭代更新不完整的初始标签，以获得具有更好鲁棒性的模型。我们的方法使用 iPS 细胞数据集的两个领域以及细胞跟踪挑战赛（CTC）倡议的细胞检测精度（DET）评估指标进行了评估，分别达到了 0.862 和 0.924 的 DET。使用来自 CTC 的公共数据集 FluoN2DH-GOWT1 测试了开发模型的可转移性，该数据集包含具有参考注释的两个数据集。我们随机删除每个标记数据的部分注释，以模拟公共数据集上的初始注释。在对两个数据集进行训练后，使用包含 10%细胞标记物的标签，DET 从 0.130 提高到 0.903 和 0.116 提高到 0.877。当使用包含 60%细胞标记物的标签进行训练时，性能优于使用监督学习方法训练的模型。这一结果表明，随着用于训练的标签质量的提高，模型的性能得到了提高。
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