自然杀伤细胞(NKC)是具有多种形态的非特异性免疫淋巴细胞。它们对癌细胞的广泛杀伤作用导致对激活NKC用于抗癌免疫疗法的关注增加。因此,了解NKCs在不同形态下的运动特征并对其在癌细胞下的集体动力学进行建模已变得至关重要。然而,在复杂背景下跟踪小型NKC会带来重大挑战,和传统的工业跟踪算法通常在NKC跟踪数据集上表现不佳。关于NKC动力学的研究仍然很少。在本文中,我们利用深度学习技术来分析NKC的形态及其关键点。在分析了DeepSORT等常见工业多目标跟踪算法在跟踪自然杀伤细胞方面的不足后,我们提出了距离级联匹配和重新搜索方法来改进现有算法,产生有希望的结果。通过处理和跟踪超过5000帧的图像,包括大约300,000个细胞,我们初步探讨了NKCs细胞形态的影响,温度,和癌细胞环境对NKC运动的影响,同时进行基本建模。本研究的主要结论如下:极化细胞更容易沿其极化方向运动,极化的维持使它们更有可能接近癌细胞;在平衡状态下,NK细胞在癌细胞表面显示玻尔兹曼分布。
Natural killer cells (NKCs) are non-specific immune lymphocytes with diverse morphologies. Their broad killing effect on cancer cells has led to increased attention towards activating NKCs for anticancer immunotherapy. Consequently, understanding the motion characteristics of NKCs under different morphologies and modeling their collective dynamics under cancer cells has become crucial. However, tracking small NKCs in complex backgrounds poses significant challenges, and conventional industrial tracking algorithms often perform poorly on NKC tracking datasets. There remains a scarcity of research on NKC dynamics. In this paper, we utilize deep learning techniques to analyze the morphology of NKCs and their key points. After analyzing the shortcomings of common industrial multi-object tracking algorithms like DeepSORT in tracking natural killer cells, we propose Distance Cascade Matching and the Re-Search method to improve upon existing algorithms, yielding promising results. Through processing and tracking over 5000 frames of images, encompassing approximately 300,000 cells, we preliminarily explore the impact of NKCs\' cell morphology, temperature, and cancer cell environment on NKCs\' motion, along with conducting basic modeling. The main conclusions of this study are as follows: polarized cells are more likely to move along their polarization direction and exhibit stronger activity, and the maintenance of polarization makes them more likely to approach cancer cells; under equilibrium, NK cells display a Boltzmann distribution on the cancer cell surface.