3D Nanoscale Tracking Data Analysis for Intracellular Organelle Movement using Machine Learning Approach

Abstract

Tracking of intracellular organelle movement such as vesicle includes crucial information in biomedicine. To achieve more accurate three-dimensional localization of the target organelle, superresolution imaging microscopy and image processing methods have been developed and applied to many nanoscale tracking systems. Although such recent advances in microscopy imaging have enabled us to gather a tremendous amount of tracking data, the details of the movement including the interaction between cytoskeletons are not yet fully explained. In the present work, we suggest a machine learning approach to clarify the problem in tracking data analysis, as an initial trial to exploit artificial intelligence in distinguishing and classifying the detail features of the vesicle-cytoskeleton interactions.