Neurite reconstruction from time-lapse sequences using co-segmentation

S. Gulyanon, N. Sharifai, Michael D. Kim, A. Chiba, G. Tsechpenakis

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

1 Scopus citations

Abstract

We introduce a novel segmentation method for time-lapse image stacks of neurites based on the co-segmentation principle. Our method aggregates information from multiple stacks to improve the segmentation task, using a neurite model and a tree similarity term. The neurite model takes into account branching characteristics, such as local shape smoothness and continuity, while the tree similarity term exploits the local branch dynamics across image stacks. Our approach improves accuracy in ambiguous regions, handling successfully out-of-focus effects and branching bifurcations. We validated our method using Drosophila sensory neuron datasets and made comparisons with existing methods.

Original languageEnglish (US)
Title of host publication2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017
PublisherIEEE Computer Society
Pages410-414
Number of pages5
ISBN (Electronic)9781509011711
DOIs
StatePublished - Jun 15 2017
Event14th IEEE International Symposium on Biomedical Imaging, ISBI 2017 - Melbourne, Australia
Duration: Apr 18 2017Apr 21 2017

Other

Other14th IEEE International Symposium on Biomedical Imaging, ISBI 2017
CountryAustralia
CityMelbourne
Period4/18/174/21/17

Keywords

  • Co-segmentation
  • Drosophila
  • Graph cuts
  • Neurite segmentation

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Fingerprint Dive into the research topics of 'Neurite reconstruction from time-lapse sequences using co-segmentation'. Together they form a unique fingerprint.

  • Cite this

    Gulyanon, S., Sharifai, N., Kim, M. D., Chiba, A., & Tsechpenakis, G. (2017). Neurite reconstruction from time-lapse sequences using co-segmentation. In 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017 (pp. 410-414). [7950549] IEEE Computer Society. https://doi.org/10.1109/ISBI.2017.7950549