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

Türetken, Engin; Wang, Xinchao; Becker, Carlos; Haubold, Carsten; Fua, Pascal

doi:10.1109/tmi.2016.2640859

Cited by 60 publications

(30 citation statements)

References 53 publications

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“…These can be simple pixel/voxel or average region intensities, or more complex local image descriptors of shapes or textures; (iii) Finally, we distinguish the segmentation method itself that implements the principle using the features . The methods range from simple thresholding 9 , 10 , hysteresis thresholding 11 , edge detection 12 , or shape matching 13 , 14 , to more sophisticated region growing 15 – 17 , machine learning 18 , 19 , or energy minimization 20 – 26 approaches.…”

Section: Introductionmentioning

confidence: 99%

An objective comparison of cell-tracking algorithms

et al. 2017

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We present a combined report on the results of three editions of the Cell Tracking Challenge, an ongoing initiative aimed at promoting the development and objective evaluation of cell tracking algorithms. With twenty-one participating algorithms and a data repository consisting of thirteen datasets of various microscopy modalities, the challenge displays today’s state of the art in the field. We analyze the results using performance measures for segmentation and tracking that rank all participating methods. We also analyze the performance of all algorithms in terms of biological measures and their practical usability. Even though some methods score high in all technical aspects, not a single one obtains fully correct solutions. We show that methods that either take prior information into account using learning strategies or analyze cells in a global spatio-temporal video context perform better than other methods under the segmentation and tracking scenarios included in the challenge.

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Section: Introductionmentioning

confidence: 99%

An objective comparison of cell-tracking algorithms

et al. 2017

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“…In Segmentation, EllipTrack approximates the shapes of cell nuclei as ellipses and segments cells by applying an ellipse-fitting algorithm to the nuclear contours (Zafari et al, 2015). This method shows improved performance to separate overlapping nuclei and has been implemented in other cell trackers (Türetken et al, 2016) (Figure S1B). In Track Linking, EllipTrack first constructs cell lineages with the Magnusson global track-linking algorithm (Magnusson et al, 2015), and then executes our local track-correction module to correct tracking mistakes (detailed below).…”

Section: The Elliptrack Pipelinementioning

confidence: 99%

EllipTrack: A Global-Local Cell-Tracking Pipeline for 2D Fluorescence Time-Lapse Microscopy

Tian

Yang

Spencer

2020

Preprint

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Time-lapse microscopy provides an unprecedented opportunity to monitor single-cell dynamics. However, tracking cells for long periods of time remains a technical challenge, especially for multi-day, large-scale movies with rapid cell migration, high cell density, and drug treatments that alter cell morphology/behavior. Here, we present EllipTrack, a global-local cell-tracking pipeline optimized for tracking such movies.EllipTrack first implements a global track-linking algorithm to construct tracks that maximize the probability of cell lineages, and then corrects tracking mistakes with a local track-correction module where tracks generated by the global algorithm are systematically examined and amended if a more probable alternative can be found.Through benchmarking, we show that EllipTrack outperforms state-of-the-art cell trackers and generates nearly error-free cell lineages for multiple large-scale movies. In addition, EllipTrack can adapt to time-and cell density-dependent changes in cell migration speeds, requires minimal training datasets, and provides a user-friendly interface. EllipTrack is available at github.com/tianchengzhe/EllipTrack.

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“…Other researcher have attempted to address the MOT problem of initialisation in a variety of ways. In [15], Türetken et al proposed a way of tracking elliptical cell populations in videos by using image segmentation and elliptical fitting to find cell candidates. They create a hierarchy of these candidates and use network flow integer programming to select the most temporally consistent cell candidates.…”

Section: Related Workmentioning

confidence: 99%

Learn-select-track: An approach to multi-object tracking

Makhura

Woods

2019

Signal Processing: Image Communication

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Object tracking algorithms rely on user input to learn the object of interest. In multi-object tracking, this can be a challenge when the user has to provide a lot of locations to track. This paper presents a new approach that reduces the need for user input in multi-tracking. The approach uses density based clustering to analyse the colours in one frame and find the best separation of colours. The colours selected from the detection are learned and used in subsequent frames to track the colours through the video. With this training approach, the user interaction is limited to selecting the colours rather than selecting the multiple location to be tracked. The training algorithm also provides online training even when training on thousands of features. Highlights Density based clustering for learning object features to track. Density based clustering for tracking objects in videos. Training and tacking without knowing the number of objects. Performance of the tacking and training algorithms.

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

Cited by 60 publications

References 53 publications

An objective comparison of cell-tracking algorithms

An objective comparison of cell-tracking algorithms

EllipTrack: A Global-Local Cell-Tracking Pipeline for 2D Fluorescence Time-Lapse Microscopy

Learn-select-track: An approach to multi-object tracking

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