Overlap-based cell tracker

Chalfoun, Joe; Cardone, Antonio; Dima, Alden A.; Allen, Daniel P.; Halter, Michael

doi:10.6028/jres.115.034

Cited by 9 publications

(4 citation statements)

References 15 publications

(16 reference statements)

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“…The description of the automated image analysis algorithms for segmentation and tracking illustrates the key operational steps and the algorithm details will be described elsewhere (26). Importantly, the segmentation algorithm relies only on the phase contrast images so that both brightly fluorescent and dimly fluorescent cells are segmented similarly.…”

Section: Automated Segmentation and Trackingmentioning

confidence: 99%

Cell cycle dependent TN‐C promoter activity determined by live cell imaging

et al. 2011

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The extracellular matrix protein tenascin-C plays a critical role in development, wound healing, and cancer progression, but how it is controlled and how it exerts its physiological responses remain unclear. By quantifying the behavior of live cells with phase contrast and fluorescence microscopy, the dynamic regulation of TN-C promoter activity is examined. We employ an NIH 3T3 cell line stably transfected with the TN-C promoter ligated to the gene sequence for destabilized green fluorescent protein (GFP). Fully automated image analysis routines, validated by comparison with data derived from manual segmentation and tracking of single cells, are used to quantify changes in the cellular GFP in hundreds of individual cells throughout their cell cycle during live cell imaging experiments lasting 62 h. We find that individual cells vary substantially in their expression patterns over the cell cycle, but that on average TN-C promoter activity increases during the last 40% of the cell cycle. We also find that the increase in promoter activity is proportional to the activity earlier in the cell cycle. This work illustrates the application of live cell microscopy and automated image analysis of a promoter-driven GFP reporter cell line to identify subtle gene regulatory mechanisms that are difficult to uncover using population averaged measurements. Published 2011 Wiley-Liss, Inc. y

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Section: Automated Segmentation and Trackingmentioning

confidence: 99%

Cell cycle dependent TN‐C promoter activity determined by live cell imaging

et al. 2011

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“…For segmented images, we can also use the overlap between segmented regions to calculate the cost ( Chalfoun et al , 2010 ; Ershov et al , 2022 ; Padovani et al , 2022 ). The flexible implementation allows us to integrate the overlap metric in addition to the distance in the LAP framework.…”

Section: Resultsmentioning

confidence: 99%

LapTrack: linear assignment particle tracking with tunable metrics

Fukai

Kawaguchi

2022

Bioinformatics

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Motivation Particle tracking is an important step of analysis in a variety of scientific fields, and is particularly indispensable for the construction of cellular lineages from live images. Although various supervised machine learning methods have been developed for cell tracking, the diversity of the data still necessitates heuristic methods that require parameter estimations from small amounts of data. For this, solving tracking as a linear assignment problem (LAP) has been widely applied and demonstrated to be efficient. However, there has been no implementation that allows custom connection costs, parallel parameter tuning with ground truth annotations, and the functionality to preserve ground truth connections, limiting the application to datasets with partial annotations. Results We developed LapTrack, a LAP-based tracker which allows including arbitrary cost functions and inputs, parallel parameter tuning, and ground-truth track preservation. Analysis of real and artificial datasets demonstrates the advantage of custom metric functions for tracking score improvement from distance-only cases. The tracker can be easily combined with other Python-based tools for particle detection, segmentation, and visualization. Availability and implementation LapTrack is available as a Python package on PyPi, and the notebook examples are shared at https://github.com/yfukai/laptrack. The data and code for this publication are hosted at https://github.com/NoneqPhysLivingMatterLab/laptrack-optimisation. Supplementary information Supplementary data are available at Bioinformatics online.

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“…For segmented images, we can also use the overlap between segmented regions to calculate the cost [7, 10, 29]. The flexible implementation allows us to integrate the overlap metric in addition to the distance in the LAP framework.We defined l (with g and s analogously) as which measures the overlap, where L i and L j are the set of pixel coordinates of the segmentation area for the particle i and j and A is a parameter.…”

Section: Resultsmentioning

confidence: 99%

LapTrack: Linear assignment particle tracking with tunable metrics

Fukai

Kawaguchi

2022

Preprint

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Motivation: Particle tracking is an important step of analysis in a variety of scientific fields, and is particularly indispensable for the construction of cellular lineages from live images. Although various supervised machine learning methods have been developed for cell tracking, the diversity of the data still necessitates heuristic methods that require parameter estimations from small amounts of data. For this, solving tracking as a linear assignment problem (LAP) has been widely applied and demonstrated to be efficient. However, there has been no implementation that allows custom connection costs, parallel parameter tuning with ground truth annotations, and the functionality to preserve ground truth connections, limiting the application to datasets with partial annotations. Results: We developed LapTrack, a LAP-based tracker which allows including arbitrary cost functions and inputs, parallel parameter tuning, and ground-truth track preservation. Analysis of real and artificial datasets demonstrates the advantage of custom metric functions for tracking score improvement. The tracker can be easily combined with other Python-based tools for particle detection, segmentation, and visualization. Availability and implementation: LapTrack is available as a Python package on PyPi, and the notebook examples are shared at https://github.com/yfukai/laptrack. The data and code for this publication are hosted at https://github.com/NoneqPhysLivingMatterLab/laptrack-optimization. Contact: ysk@yfukai.net

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Overlap-based cell tracker

Cited by 9 publications

References 15 publications

Cell cycle dependent TN‐C promoter activity determined by live cell imaging

Cell cycle dependent TN‐C promoter activity determined by live cell imaging

LapTrack: linear assignment particle tracking with tunable metrics

LapTrack: Linear assignment particle tracking with tunable metrics

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