发表机构
Institut Pasteur; CNRS; LTCI, Telecom Paris, Institut Polytechnique de Paris; Department of Biological Sciences, Columbia University（巴斯德研究所; 法国国家科学研究中心; 巴黎理工学院巴黎电信学院LTCI; 哥伦比亚大学生物科学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对延时荧光成像中粒子跟踪问题，引入视觉特征自监督学习，利用视觉和位置距离拼接轨迹段，在普通水螅神经元延时荧光序列上验证，提高了拼接精度，减少了先前算法一半的错误。
AI 中文摘要
在延时荧光成像中，单粒子跟踪是监测感兴趣物体动态并提取生物过程信息的有力工具。然而，被跟踪粒子可能会被遮挡且具有间歇性可检测性。当这些现象持续几帧时，跟踪算法往往会为同一粒子生成多个轨迹段。在这项工作中，我们引入视觉特征的自监督学习来比较被跟踪粒子，并利用视觉和位置距离来稳健地拼接代表同一粒子的轨迹段。我们在普通水螅神经元的延时荧光序列上展示了我们拼接框架的性能。结果显示出高拼接精度，并且将先前算法在相同数据上产生的错误减少了一半。
英文摘要
In time-lapse fluorescence imaging, single-particle-tracking is a powerful tool to monitor the dynamics of objects of interest, and extract information about biological processes. However, tracked particles can be subject to occlusion and intermittent detectability. When these phenomena persist over a few frames, tracking algorithms tend to produce multiple tracklets for the same particle. In this work, we introduce self-supervised learning of visual features to compare tracked particles, and we exploit both visual and positional distances to robustly stitch tracklets representing the same particle. We demonstrate the performance of our stitching framework on time-lapse fluorescence sequences of Hydra vulgaris neurons. Results show high stitching precision, and reduction of errors made by previous algorithms on the same data by a factor of two.
Journal ref2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), Apr 2023, Cartagena, France. pp.1-5
