Analysis of High-Throughput Microscopy Videos: Catching Up with Cell Dynamics

A. Arbelle, N. Drayman, M. Bray, U. Alon, A. Carpenter, T. Riklin Raviv

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations

Abstract

We present a novel framework for high-throughput cell lineage analysis in time-lapse microscopy images. Our algorithm ties together two fundamental aspects of cell lineage construction, namely cell segmentation and tracking, via a Bayesian inference of dynamic models. The proposed contribution exploits the Kalman inference problem by estimating the time-wise cell shape uncertainty in addition to cell trajectory. These inferred cell properties are combined with the observed image measurements within a fast marching (FM) algorithm, to achieve posterior probabilities for cell segmentation and association. Highly accurate results on two different cell-tracking datasets are presented.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention – MICCAI 2015 - 18th International Conference, Proceedings
EditorsAlejandro F. Frangi, Nassir Navab, Joachim Hornegger, William M. Wells
PublisherSpringer Verlag
Pages218-225
Number of pages8
ISBN (Print)9783319245737
DOIs
StatePublished - 2015
Event18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015 - Munich, Germany
Duration: 5 Oct 20159 Oct 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9351
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015
Country/TerritoryGermany
CityMunich
Period5/10/159/10/15

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

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