Three-dimensional super line-localization in low signal-to-noise microscope images via prior-apprised unsupervised learning (PAUL)

Shuhui Yin, M. Junaid Amin, Nyssa T. Emerson, Haw Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Biological processes such as processive enzyme turnover and intracellular cargo trafficking involve the dynamic motion of a small “particle” along a curvilinear biopolymer track. To understand these processes that occur across multiple length and time scales, one must acquire both the trajectory of the particle and the position of the track along which it moves, possibly by combining high-resolution single-particle tracking with conventional microscopy. Yet, usually there is a significant resolution mismatch between these modalities: while the tracked particle is localized with a precision of 10 nm, the image of the surroundings is limited by optical diffraction, with 200 nm lateral and 500 nm axial resolutions. Compared to the particle’s trajectory, the surrounding curvilinear structure appears as a blurred and noisy image. This disparity in the spatial resolutions of the particle trajectory and the surrounding curvilinear structure image makes data reconstruction, as well as interpretation, particularly challenging. Analysis is further complicated when the curvilinear structures are oriented arbitrarily in 3D space. Here, we present a prior-apprised unsupervised learning (PAUL) approach to extract information from 3D images where the underlying features resemble a curved line such as a filament or microtubule. This three-stage framework starts with a Hessian-based feature enhancement, which is followed by feature registration, where local line segments are detected on repetitively sampled subimage tiles. In the final stage, statistical learning, segments are clustered based on their geometric relationships. Principal curves are then approximated from each segment group via statistical tools including principal component analysis, bootstrap and kernel transformation. This procedure is characterized on simulated images, where sub-voxel medium deviations from true curves have been achieved. The 3D PAUL approach has also been implemented for successful line localization in experimental 3D images of gold nanowires obtained using a multifocal microscope. This work not only bridges the resolution gap between two microscopy modalities, but also allows us to conduct 3D super line-localization imaging experiments, without using super-resolution techniques.

Original languageEnglish (US)
Title of host publicationApplications of Digital Image Processing XLIII
EditorsAndrew G. Tescher, Touradj Ebrahimi
PublisherSPIE
ISBN (Electronic)9781510638266
DOIs
StatePublished - 2020
EventApplications of Digital Image Processing XLIII 2020 - Virtual, Online, United States
Duration: Aug 24 2020Sep 4 2020

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11510
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceApplications of Digital Image Processing XLIII 2020
CountryUnited States
CityVirtual, Online
Period8/24/209/4/20

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Keywords

  • Clustering
  • Principal curves
  • Resolution matching
  • Super resolution

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