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

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 explo...

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Bibliographic Details
Published inMedical Image Computing and Computer-Assisted Intervention – MICCAI 2015 pp. 218 - 225
Main Authors Arbelle, A., Drayman, N., Bray, M., Alon, U., Carpenter, A., Raviv, T. Riklin
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Active Contour
Cell Association
Cell Segmentation
Cell Tracking
Signed Distance Function
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Summary: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.
ISBN:9783319245737
3319245732
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-24574-4_26