Mark R Winter, Cheng Fang, Gary Banker, Badrinath Roysam, Andrew R Cohen
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引用次数: 41
摘要
多时间关联跟踪(multi - temporal Association Tracking, MAT)是一种新的基于图的生物多目标跟踪方法,与基于二部匹配的方法相比,它降低了错误率和实现复杂度。数据关联问题通过使用近似贝叶斯后验关联概率的基于图的成本函数来解决未来检测数据的窗口。MAT已应用于数百个图像序列,跟踪细胞器和囊泡,以量化伴随神经退行性疾病(如亨廷顿病和多发性硬化症)的轴突运输缺陷,并量化响应治疗干预的运输变化。
Axonal transport analysis using Multitemporal Association Tracking.
Multitemporal Association Tracking (MAT) is a new graph-based method for multitarget tracking in biological applications that reduces the error rate and implementation complexity compared to approaches based on bipartite matching. The data association problem is solved over a window of future detection data using a graph-based cost function that approximates the Bayesian a posteriori association probability. MAT has been applied to hundreds of image sequences, tracking organelle and vesicles to quantify the deficiencies in axonal transport that can accompany neurodegenerative disorders such as Huntington's Disease and Multiple Sclerosis and to quantify changes in transport in response to therapeutic interventions.