Tracking cells over time is crucial in the fields of computer vision and biomedical science. Studying neutrophils and their migratory profile is the highly topical fields in inflammation research due to determining role of these cells during immune responses. As neutrophils generally are of various shapes and motion, it remains challenging to track and describe their behaviours from multi-dimensional microscopy datasets. In this study, we propose a robust novel multi-channel feature learning (MCFL) model inspired by deep learning to extract the complex behaviour of neutrophils moved in time lapse images. In this model, the convolutional neural networks along with cell relocation distance and orientation channels learn the robust significant spatial and temporal features of an individual neutrophil. Additionally, we also proposed a new cell tracking framework to detect and track neutrophils in the original time-laps microscopy images, entails sampling, observation, and visualisation functions. Our proposed cell tracking-based-multi channel feature learning method has remarkable performance in rectifying common cell tracking problem compared with state-of the-art methods.

中文翻译：

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使用多通道特征学习跟踪斑马鱼模型中的中性粒细胞迁移

随着时间的推移跟踪细胞在计算机视觉和生物医学科学领域至关重要。由于确定这些细胞在免疫反应中的作用，研究中性粒细胞及其迁移特征是炎症研究中高度关注的领域。由于中性粒细胞通常具有各种形状和运动，因此从多维显微镜数据集跟踪和描述它们的行为仍然具有挑战性。在这项研究中，我们提出了一种受深度学习启发的强大的新型多通道特征学习 (MCFL) 模型，以提取在延时图像中移动的中性粒细胞的复杂行为。在这个模型中，卷积神经网络连同细胞重定位距离和方向通道一起学习单个中性粒细胞的稳健的显着空间和时间特征。此外，我们还提出了一个新的细胞跟踪框架来检测和跟踪原始延时显微镜图像中的中性粒细胞，需要采样、观察和可视化功能。与最先进的方法相比，我们提出的基于细胞跟踪的多通道特征学习方法在纠正常见的细胞跟踪问题方面具有显着的性能。