Automated tracking of unmarked cells migrating in three-dimensional matrices applied to anti-cancer drug screening

Adanja, Ivan; Debeir, Olivier; Mégalizzi, Veronique; Kiss, Róbert; Warzée, Nadine; Decaestecker, Christine

doi:10.1016/j.yexcr.2009.10.004

Cited by 28 publications

(35 citation statements)

References 20 publications

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“…This is a serious problem in the biological community and no relevant literature [13], [20], [21] has correctly solved it. EIF classifies the metrics (β, β(0), H) behavior using empirical thresholds and identifies tracking confusion.…”

Section: Trackermentioning

confidence: 99%

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Event Indicator Function classifier for identifying cell tracking errors and phenotypes

Konda

Chakravorty

Challa

2012

2012 4th International Conference on Intelligent Human Computer Interaction (IHCI)

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Biologists estimate parameters such as division time, death time, time to differentiate into specialized cell in order to model cell behavior and to develop novel ways to fight diseases such as Cancer, HIV and others. One of the critical steps of such analysis of cells in video microscopy is to follow each of the cells through their generations and collect relevant information. Variability of cell density and dynamics in different video, hamper portability of existing automated cell tracking systems across videos. These errors have to be identified and corrected using human assistance to achieve tracker portability across videos. In this paper, we propose Event Indicator Function (EIF) classifier to predict the tracking errors and cell phenotypes (division and death) frame-by-frame using a set of features (metrics) that are collected during tracking. EIF classifier models the metrics using empirical thresholds to identify the errors and phenotypes. Finally, EIF classifier performance has been evaluated on variety of microscopic videos that differ both in cell density and dynamics, illustrated results show the significance of the proposed classifier.

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Section: Trackermentioning

confidence: 99%

“…Moreover, it does not have the capability to identify and resolve the tracking confusion (data-association), especially in densely populated cell culture. When two tracks come very close to each other, they are likely to get confused [21]. [13] proposes to sort automatically constructed links according to their costs.…”

Section: Introductionmentioning

confidence: 99%

Event Indicator Function classifier for identifying cell tracking errors and phenotypes

Konda

Chakravorty

Challa

2012

2012 4th International Conference on Intelligent Human Computer Interaction (IHCI)

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“…ELL migration is one of the key cellular processes that is associated with a wide range of biological phenomena including embryogenesis, inflammation, wound healing, tumour development [1], [2], and its accurate estimation plays an important role in cell and molecular biology research [3]. Typically, cell migration is evaluated in time-lapse image data and the current practice involves a labour intensive (manual) procedure that is applied to analyze/track the cells in all frames of the image sequence.…”

Section: Introductionmentioning

confidence: 99%

“…Typically, cell migration is evaluated in time-lapse image data and the current practice involves a labour intensive (manual) procedure that is applied to analyze/track the cells in all frames of the image sequence. With the advent of modern microscopy imaging modalities the amount of information required to be analyzed by clinical experts has substantially increased and in many situations the standard manual procedure has become impractical [2]. The limitations of the manual procedures when applied to the estimation of cell migration opened a new research area that addresses the development of computer vision-based automatic cellular tracking algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Novel Framework for Cellular Tracking and Mitosis Detection in Dense Phase Contrast Microscopy Images

Thirusittampalam

Hossain

Ghita

et al. 2013

IEEE J. Biomed. Health Inform.

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Abstract-The aim of this paper is to detail the development of a novel tracking framework that is able to extract the cell motility indicators and to determine the cellular division (mitosis) events in large time-lapse phase-contrast image sequences. To address the challenges induced by non-structured (random) motion, cellular agglomeration, and cellular mitosis, the process of automatic (unsupervised) cell tracking is carried out in a sequential manner, where the inter-frame cell association is achieved by assessing the variation in the local cellular structures in consecutive frames of the image sequence. In our study a strong emphasis has been placed on the robust use of the topological information in the cellular tracking process and in the development of targeted pattern recognition techniques that were designed to redress the problems caused by segmentation errors, and to precisely identify mitosis using a backward (reversed) tracking strategy. The proposed algorithm has been evaluated on dense phase contrast cellular data and the experimental results indicate that the proposed algorithm is able to accurately track epithelial and endothelial cells in time-lapse image sequences that are characterized by low contrast and high level of noise. Our algorithm achieved 86.10% overall tracking accuracy and 90.12% mitosis detection accuracy.

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“…[1,9]. This area of research has received substantial interest from cell and molecular scientists since the understanding of mechanisms that stimulate/reduce the cell motility has important practical implications [11].…”

Section: Introductionmentioning

confidence: 99%

A Novel Framework for Tracking In-vitro Cells in Time-lapse Phase Contrast Data

Thirusittampalam¹,

Hossain²,

Ghita³

et al. 2010

Procedings of the British Machine Vision Conference 2010

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With the proliferation of modern microscopy imaging technologies the amount of data that has to be analysed by biologists is constantly increasing and as a result the development of automatic approaches that are able to track cellular structures in timelapse images has become an important field of research. The aim of this paper is to detail the development of a novel tracking framework that is designed to extract the cell motility indicators in phase-contrast image sequences. To address issues that are caused by nonstructured (random) motion and cellular agglomeration, cell tracking is formulated as a sequential process where the inter-frame cell association is achieved by assessing the variation in the local structures contained in consecutive frames of the image sequence. We have evaluated the proposed algorithm on dense phase contrast cellular data and the reported results indicate that the developed algorithm is able to accurately track MadinDarby Canine Kidney (MDCK) Epithelial Cells in image data that is characterised by low contrast and high level of noise.

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Automated tracking of unmarked cells migrating in three-dimensional matrices applied to anti-cancer drug screening

Cited by 28 publications

References 20 publications

Event Indicator Function classifier for identifying cell tracking errors and phenotypes

Event Indicator Function classifier for identifying cell tracking errors and phenotypes

A Novel Framework for Cellular Tracking and Mitosis Detection in Dense Phase Contrast Microscopy Images

A Novel Framework for Tracking In-vitro Cells in Time-lapse Phase Contrast Data

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