Efficient cell segmentation and tracking of developing plant meristem

Mkrtchyan, Katya; Singh, Damanpreet; Liu, M.; Reddy, V.; Roy-Chowdhury, Amit K.; Gopi, M.

doi:10.1109/icip.2011.6116040

Cited by 19 publications

(11 citation statements)

References 7 publications

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“…Future work would include the integration of this spatio-temporal tracking method with our image analysis components such as segmentation (Mkrtchyan et al, 2011), registration (Mkrtchyan et al, 2013) and the cell resolution 3D reconstruction methods Chakraborty et al, 2011) to design a complete 4D image analysis pipeline. This pipeline could be effective for generating cell division and cell growth statistics in a fully automated, high-throughput manner.…”

Section: Discussionmentioning

confidence: 99%

“…The choice of the 2D segmentation algorithm is largely data-specific. For our experiments on the SAM tissues, we use an adaptive Watershed segmentation method (Mkrtchyan et al, 2011) that learns the 'h-minima' threshold directly from the image data so that a uniformity in cell sizes is maintained as a result of the segmentation. This method works satisfactorily for SAM cells as, in general, all SAM cells on a 2D confocal slice have similar sizes.…”

Section: Data Collection and Preprocessingmentioning

confidence: 99%

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Context aware spatio-temporal cell tracking in densely packed multilayer tissues

Chakraborty

Roy-Chowdhury

2015

Medical Image Analysis

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Section: Discussionmentioning

confidence: 99%

Section: Data Collection and Preprocessingmentioning

confidence: 99%

Context aware spatio-temporal cell tracking in densely packed multilayer tissues

Chakraborty

Roy-Chowdhury

2015

Medical Image Analysis

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“…A similar algorithm with weighted average fitting residuals is reported in [65] to locate nuclei in fluorescence microscopy images. Another HIT algorithm with automatic selection of the h value for cell detection is presented in [66], which exploits the variance in the cell areas to evaluate the segmentation quality and the optimal h corresponds to the case with a minimum area variance. The h value in HAT can also be determined in terms of the intensity contrast between the nuclei and the background in phase contrast microscopy images [47], [48].…”

Section: Nucleus and Cell Detection Methodsmentioning

confidence: 99%

Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review

Xing

Yang

2016

IEEE Rev. Biomed. Eng.

407

220

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Digital pathology and microscopy image analysis is widely used for comprehensive studies of cell morphology or tissue structure. Manual assessment is labor intensive and prone to inter-observer variations. Computer-aided methods, which can significantly improve the objectivity and reproducibility, have attracted a great deal of interest in recent literatures. Among the pipeline of building a computer-aided diagnosis system, nucleus or cell detection and segmentation play a very important role to describe the molecular morphological information. In the past few decades, many efforts have been devoted to automated nucleus/cell detection and segmentation. In this review, we provide a comprehensive summary of the recent state-of-the-art nucleus/cell segmentation approaches on different types of microscopy images including bright-field, phase-contrast, differential interference contrast (DIC), fluorescence, and electron microscopies. In addition, we discuss the challenges for the current methods and the potential future work of nucleus/cell detection and segmentation.

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“…7. The problem of cell tracking is to associate these spatio-temporal Methodology: Each 2D image slice in the 4D confocal image stack is segmented into individual cell slices using an adaptive Watershed segmentation method [42] that learns the 'h-minima' threshold directly from the image data so that a uniformity in cell sizes is maintained as a result of the segmentation. Further the 3D image stacks are temporally registered using a landmark-based registration scheme [43].…”

Section: Spatio-temporal Cell Trackingmentioning

confidence: 99%

Network Consistent Data Association

Chakraborty

Das²,

Roy-Chowdhury

2016

IEEE Trans. Pattern Anal. Mach. Intell.

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Existing data association techniques mostly focus on matching pairs of data-point sets and then repeating this process along space-time to achieve long term correspondences. However, in many problems such as person re-identification, a set of data-points may be observed at multiple spatio-temporal locations and/or by multiple agents in a network and simply combining the local pairwise association results between sets of data-points often leads to inconsistencies over the global space-time horizons. In this paper, we propose a Novel Network Consistent Data Association (NCDA) framework formulated as an optimization problem that not only maintains consistency in association results across the network, but also improves the pairwise data association accuracies. The proposed NCDA can be solved as a binary integer program leading to a globally optimal solution and is capable of handling the challenging data-association scenario where the number of data-points varies across different sets of instances in the network. We also present an online implementation of NCDA method that can dynamically associate new observations to already observed data-points in an iterative fashion, while maintaining network consistency. We have tested both the batch and the online NCDA in two application areas-person re-identification and spatio-temporal cell tracking and observed consistent and highly accurate data association results in all the cases.

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Efficient cell segmentation and tracking of developing plant meristem

Cited by 19 publications

References 7 publications

Context aware spatio-temporal cell tracking in densely packed multilayer tissues

Context aware spatio-temporal cell tracking in densely packed multilayer tissues

Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review

Network Consistent Data Association

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