Prior-Apprised Unsupervised Learning of Subpixel Curvilinear Features in Low Signal/Noise Images.

ABSTRACT: Many biophysical problems involve molecular and nanoscale targets moving next to a curvilinear track, e.g., a cytosolic cargo transported by motor proteins moving along a microtubule. For this type of problem, fluorescence imaging is usually the primary tool of choice. There is, however, an ∼20-fold mismatch between target-localization precision and track-imaging resolution such that questions requiring high-fidelity definition of the target's track remain inaccessible. On the other hand, if the contextual image of the tracks can be refined to a level comparable to that of the target, many intuitive yet mechanistically important issues can begin to be addressed. This work demonstrates that it is possible to statistically infer, to subpixel precision, curvilinear features in a low signal/no

SUBMITTER: Yin S 

PROVIDER: S-EPMC7231927 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature