基于中轴变换的视频显微镜鞭毛自动识别
Automated identification of flagella from videomicroscopy via the medial axis transform
摘要：
鞭毛普遍存在于真核生物中，是一种研究充分的细胞器，众所周知，它在多种生物中负责运动。在他们的研究中通常需要的是成像的能力，并随后使用视频显微镜跟踪一个或多个鞭毛的运动，需要在一系列帧内的鞭毛的数字隔离和位置。这样的过程目前一般需要一些研究人员的输入，提供一些手动估计或依赖于实验特定的启发式，以正确识别和跟踪鞭毛的运动。在这里，我们提出了一个全自动的方法，鞭毛鉴定从视频显微镜的基础上，鞭毛是近似恒定的宽度，当通过显微镜观察。我们证明了该算法的有效性，通过应用程序捕获视频显微镜的利什曼原虫墨西哥，寄生单鞭毛虫的家庭锥虫科。提供了用于鞭毛识别的ImageJ宏，并且通过这种无监督的方法实现了高准确度和显著的通量，获得了与密切相关的物种的先前研究质量相当的结果，但不需要辅助测量或开发专门的启发式方法，从而通常能够自动生成来自视频显微镜的鞭毛跳动的数字化运动学描述。
英文摘要：
Ubiquitous in eukaryotic organisms, the flagellum is a well-studied organelle that is well-known to be responsible for motility in a variety of organisms. Commonly necessitated in their study is the capability to image and subsequently track the movement of one or more flagella using videomicroscopy, requiring digital isolation and location of the flagellum within a sequence of frames. Such a process in general currently requires some researcher input, providing some manual estimate or reliance on an experiment-specific heuristic to correctly identify and track the motion of a flagellum. Here we present a fully-automated method of flagellum identification from videomicroscopy based on the fact that the flagella are of approximately constant width when viewed by microscopy. We demonstrate the effectiveness of the algorithm by application to captured videomicroscopy of Leishmania mexicana, a parasitic monoflagellate of the family Trypanosomatidae. ImageJ Macros for flagellar identification are provided, and high accuracy and remarkable throughput are achieved via this unsupervised method, obtaining results comparable in quality to previous studies of closely-related species but achieved without the need for precursory measurements or the development of a specialised heuristic, enabling in general the automated generation of digitised kinematic descriptions of flagellar beating from videomicroscopy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
