Fast, accurate reconstruction of cell lineages from large-scale fluorescence microscopy data

Amat, Fernando; Lemon, William C.; Mossing, Daniel P.; McDole, Katie; Wan, Yinan; Branson, Kristin; Myers, Eugene W.; Keller, Philipp J.

doi:10.1038/nmeth.3036

Cited by 264 publications

(275 citation statements)

References 38 publications

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“…To this end, we introduce a simple yet effective network flow integer programming formulation. We show that ECLIP improves trajectories and yields superior detection performance on various datasets as compared to recent approaches [1], [19], [20], [2], including the ones that performed best on the above-mentioned cell-tracking challenges [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Network Flow Integer Programming to Track Elliptical Cells in Time-Lapse Sequences

Türetken

Wang

Becker

et al. 2017

IEEE Trans. Med. Imaging

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Abstract-We propose a novel approach to automatically tracking elliptical cell populations in time-lapse image sequences. Given an initial segmentation, we account for partial occlusions and overlaps by generating an over-complete set of competing detection hypotheses. To this end, we fit ellipses to portions of the initial regions and build a hierarchy of ellipses, which are then treated as cell candidates. We then select temporally consistent ones by solving to optimality an integer program with only one type of flow variables. This eliminates the need for heuristics to handle missed detections due to partial occlusions and complex morphology. We demonstrate the effectiveness of our approach on a range of challenging sequences consisting of clumped cells and show that it outperforms state-of-the-art techniques.

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Section: Introductionmentioning

confidence: 99%

Network Flow Integer Programming to Track Elliptical Cells in Time-Lapse Sequences

Türetken

Wang

Becker

et al. 2017

IEEE Trans. Med. Imaging

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“…Unlike VR scenarios that are commonly generated by computer graphics or a 360-degree camera (11,12), the advent of light-sheet fluorescence microscopy (LSFM) (13)(14)(15)(16)(17)(18)(19)(20)(21) allows for capturing of physiological events in the 3D or 4D domain that can be further adapted to VR headsets, such as Google Cardboard or Daydream, for effective VR visualization. In comparison with wide-field or confocal microscopy, LSFM enables multidimensional imaging at the single-cell resolution (22)(23)(24)(25)(26)(27)(28)(29), which is critical for integrating high-fidelity physiological research with VR visualization. 4D LSFM further enables in vivo imaging of contracting embryonic hearts in zebrafish (Danio rerio) (30,31), Caenorhabditis elegans (32,33), and Drosophila melanogaster (34,35).…”

Section: Introductionmentioning

confidence: 99%

Integrating light-sheet imaging with virtual reality to recapitulate developmental cardiac mechanics

Ding¹,

Abiri²,

Abiri³

et al. 2017

JCI Insight

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“…In practice, this results in similar quality images compared to for example spinning disc confocal acquisition 15 . Consequently, this enables reliable extraction of features like cell membranes or nuclei, e.g., for cell lineage tracing 15,19 .…”

Section: Introductionmentioning

confidence: 99%

Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development

Icha

Schmied

Sidhaye

et al. 2016

JoVE

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Light sheet fluorescence microscopy (LSFM) is gaining more and more popularity as a method to image embryonic development. The main advantages of LSFM compared to confocal systems are its low phototoxicity, gentle mounting strategies, fast acquisition with high signal to noise ratio and the possibility of imaging samples from various angles (views) for long periods of time. Imaging from multiple views unleashes the full potential of LSFM, but at the same time it can create terabyte-sized datasets. Processing such datasets is the biggest challenge of using LSFM. In this protocol we outline some solutions to this problem. Until recently, LSFM was mostly performed in laboratories that had the expertise to build and operate their own light sheet microscopes. However, in the last three years several commercial implementations of LSFM became available, which are multipurpose and easy to use for any developmental biologist. This article is primarily directed to those researchers, who are not LSFM technology developers, but want to employ LSFM as a tool to answer specific developmental biology questions.Here, we use imaging of zebrafish eye development as an example to introduce the reader to LSFM technology and we demonstrate applications of LSFM across multiple spatial and temporal scales. This article describes a complete experimental protocol starting with the mounting of zebrafish embryos for LSFM. We then outline the options for imaging using the commercially available light sheet microscope. Importantly, we also explain a pipeline for subsequent registration and fusion of multiview datasets using an open source solution implemented as a Fiji plugin. While this protocol focuses on imaging the developing zebrafish eye and processing data from a particular imaging setup, most of the insights and troubleshooting suggestions presented here are of general use and the protocol can be adapted to a variety of light sheet microscopy experiments.

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Fast, accurate reconstruction of cell lineages from large-scale fluorescence microscopy data

Cited by 264 publications

References 38 publications

Network Flow Integer Programming to Track Elliptical Cells in Time-Lapse Sequences

Network Flow Integer Programming to Track Elliptical Cells in Time-Lapse Sequences

Integrating light-sheet imaging with virtual reality to recapitulate developmental cardiac mechanics

Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development

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