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Intersection To Overpass: Instance Segmentation On Filamentous Structures With An Orientation-Aware Neural Network

Yi Liu1, Abhishek Kolagunda1, Wayne Treible1

  • 1University of Delaware, 18 Amstel Ave, Newark, DE, USA 19716.

Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops
|April 16, 2021
PubMed
Summary

This study introduces an orientation-aware neural network for segmenting individual biological filaments. The method effectively separates filaments at junctions, improving analysis of complex structures like microtubules.

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Area of Science:

  • Biological imaging
  • Computational biology
  • Machine learning for image analysis

Background:

  • Filamentous structures are crucial in biological systems.
  • Instance-level segmentation of filaments is challenging due to complex architecture, uniform appearance, and image quality.
  • Accurate filament extraction is vital for quantifying biological processes.

Purpose of the Study:

  • To develop an advanced method for individual filament extraction.
  • To overcome limitations of existing segmentation techniques for filamentous structures.
  • To improve the analysis and quantification of biological processes involving filaments.

Main Methods:

  • Introduction of an orientation-aware neural network with six orientation-associated branches.
  • Each branch is specialized for detecting filaments within a specific orientation range.
  • Implementation of a terminus pairing algorithm to regroup filaments and achieve instance extraction.

Main Results:

  • The proposed method successfully separates filaments at junctions and handles intersections.
  • Experiments on synthetic and real microscopy (microtubule) datasets demonstrate superior performance.
  • The approach shows versatility by performing well on road network datasets.

Conclusions:

  • The orientation-aware neural network provides a robust solution for individual filament extraction.
  • This method significantly advances the capability to analyze complex filamentous structures in biological imaging.
  • The technique offers a valuable tool for researchers in cell biology and related fields.