Graphical model for joint segmentation and tracking of multiple dividing cells

Schiegg, Martin; Hanslovsky, Philipp; Haubold, Carsten; Koethe, Ullrich; Hufnagel, Lars; Hamprecht, Fred A.

doi:10.1093/bioinformatics/btu764

Cited by 82 publications

(73 citation statements)

References 16 publications

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“…This is useful to generate segmentation or pose candidates as needed by joint segmentation and tracking procedures, e.g. [13,24]. Panorama Stitching: In our motivation in Sect.…”

Section: Selection Of Segmentation Hypothesesmentioning

confidence: 99%

“…This can e.g. be useful for learning [16], tracking-by-detection methods that allow competing hypotheses [13,18,24], or for re-ranking [28] solutions based on higher order features which would be prohibitively complex for the original optimization problem. If these M solutions are required to differ in more than one label, the problem is referred to as diverse M -best [2,14,22].…”

Section: Introductionmentioning

confidence: 99%

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Diverse M-Best Solutions by Dynamic Programming

Uhlmann

Unser

Hamprecht

2017

Lecture Notes in Computer Science

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Abstract. Many computer vision pipelines involve dynamic programming primitives such as finding a shortest path or the minimum energy solution in a tree-shaped probabilistic graphical model. In such cases, extracting not merely the best, but the set of M -best solutions is useful to generate a rich collection of candidate proposals that can be used in downstream processing. In this work, we show how M -best solutions of tree-shaped graphical models can be obtained by dynamic programming on a special graph with M layers. The proposed multi-layer concept is optimal for searching M -best solutions, and so flexible that it can also approximate M -best diverse solutions. We illustrate the usefulness with applications to object detection, panorama stitching and centerline extraction.

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“…This is useful to generate segmentation or pose candidates as needed by joint segmentation and tracking procedures, e.g. [13,24]. Panorama Stitching: In our motivation in Sect.…”

Section: Selection Of Segmentation Hypothesesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Diverse M-Best Solutions by Dynamic Programming

Uhlmann

Unser

Hamprecht

2017

Lecture Notes in Computer Science

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“…1, groups of cells that appear clumped together in some frames can only be told apart when considering the sequence as a whole. The approach proposed in [2] addresses this issue by introducing a factor graph for joint cell segmentation and tracking. Inference is then achieved by solving a relatively complex integer program that involves several types of variables and constraints.…”

Section: Introductionmentioning

confidence: 99%

“…2. We then model critical cell events, such as migration and division, using flow variables and resolve the conflicts by solving the resulting integer program, which is conceptually much simpler than those of earlier approaches [13], [1], [16], [2] and yields fewer variables and constraints. We will refer to our approach as tracking Elliptical Cells using network fLow Integer Programming (ECLIP).…”

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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“…Segmentation-independent methods can be computationally expensive when fine spatial or temporal sampling is necessary, for instance in the case of rapidly moving cells. Thus, integrating segmentation and tracking can be beneficial for the effectiveness of both approaches (Wahlby et al, 2004;Schiegg et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Automated cell tracking identifies mechanically oriented cell divisions during Drosophila axis elongation

et al. 2017

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Embryos extend their anterior-posterior (AP) axis in a conserved process known as axis elongation. Drosophila axis elongation occurs in an epithelial monolayer, the germband, and is driven by cell intercalation, cell shape changes, and oriented cell divisions at the posterior germband. Anterior germband cells also divide during axis elongation. We developed image analysis and pattern-recognition methods to track dividing cells from confocal microscopy movies in a generally applicable approach. Mesectoderm cells, forming the ventral midline, divided parallel to the AP axis, while lateral cells displayed a uniform distribution of division orientations. Mesectoderm cells did not intercalate and sustained increased AP strain before cell division. After division, mesectoderm cell density increased along the AP axis, thus relieving strain. We used laser ablation to isolate mesectoderm cells from the influence of other tissues. Uncoupling the mesectoderm from intercalating cells did not affect cell division orientation. Conversely, separating the mesectoderm from the anterior and posterior poles of the embryo resulted in uniformly oriented divisions. Our data suggest that mesectoderm cells align their division angle to reduce strain caused by mechanical forces along the AP axis of the embryo.

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Graphical model for joint segmentation and tracking of multiple dividing cells

Abstract: Source code and the 3D+t Drosophila dataset along with our manual annotations will be freely available on http://hci.iwr.uni-heidelberg.de/MIP/Research/tracking/

Cited by 82 publications

References 16 publications

Diverse M-Best Solutions by Dynamic Programming

Diverse M-Best Solutions by Dynamic Programming

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

Automated cell tracking identifies mechanically oriented cell divisions during Drosophila axis elongation

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