Using Particle Filter to Track and Model Microtubule Dynamics

Kong, Koon Yin; Marcus, Adam I.; Giannakakou, Paraskevi; Wang, May D.

doi:10.1109/icip.2007.4379879

Cited by 9 publications

(9 citation statements)

References 9 publications

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“…Also, more work can be done in the tip frame linking step by using more sophisticated techniques, such as particle filtering [9] and Kalman filtering[10]. In [10], an Active Shape Model (ASM) is integrated with Kalman filtering to utilize shape information along the longitudinal direction of the MTs and reduce noise and clutter effects.…”

Section: Discussionmentioning

confidence: 99%

Automatic tip selection for microtubule dynamics quantification

Malavé

Zhao

Kong

et al. 2010

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology

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Microtubule (MT) dynamics quantification includes modeling of elongation, rapid shortening, and pauses. It indicates the effect of the cancer treatment drug paclitaxel because the drug causes MTs to bundle, which will in turn inhibit successful mitosis of cancerous cells. Thus, automatic MT dynamics analysis has been researched intensely because it allows for faster evaluation of potential cancer treatments and better understanding of drug effects on a cell. However, most current literatures still use manual initialization. In this work, we propose an automatic initialization algorithm that selects isolated and active tips for tracking. We use a Gaussian match filter to enhance the MT structures, and a novel technique called Pixel Nucleus Analysis (PNA) for isolated MT tip detection. To find dynamic tips, we applied a masked FFT in the temporal domain followed by K-means clustering. To evaluate the selected tips, we used a low level tip linking algorithm, and show the results of applying the algorithm to a model image and five MCF-7 breast cancer cell line images captured using fluorescent confocal microscopy. Finally, we compare tip selection criteria with existing automatic selection algorithms. We conclude that the proposed analysis is an effective technique based on three criteria which include outer region selection, separation, and MT dynamics.

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Section: Discussionmentioning

confidence: 99%

Automatic tip selection for microtubule dynamics quantification

Malavé

Zhao

Kong

et al. 2010

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology

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“…This is one of the major advantages when compared with approaches that work in a 4-dimensional space in [9] and the even higher dimensional space in [8]. To systematically search along an over-grown SOAC for the optimal tip location, we employ the widely used Sequential Importance Resampling (SIR) PF [11] to estimate the locations of both B-end and P-end of a filament.…”

Section: Actin Filament Tracking Using Particle Filtersmentioning

confidence: 99%

“…A limiting factor in this work was that its high-dimensional state space added complexity to modeling and tracking computation. Kong et al [9] employed a PF-based method similar to [8] to track MT tips. In this work, MT tip locations were estimated recursively using PF, and then MT bodies were segmented using active contours based on those estimations.…”

Section: Introductionmentioning

confidence: 99%

“…This novel framework naturally integrates filament body constraints to tip estimation. This is a computationally more efficient method as compared with the 4-dimensional space in [9] and the even higher dimensional space in [8], and different from other filament tracking algorithms which either track tips [7], [8], [9] or entire bodies [6] of MTs. Experimental evaluation and comparison showed accurate and robust tracking performance of our algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Actin Filament Tracking Based on Particle Filters and Stretching Open Active Contour Models

Shen

Vavylonis

et al. 2009

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009

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Abstract. We introduce a novel algorithm for actin filament tracking and elongation measurement. Particle Filters (PF) and Stretching Open Active Contours (SOAC) work cooperatively to simplify the modeling of PF in a one-dimensional state space while naturally integrating filament body constraints to tip estimation. Our algorithm reduces the PF state spaces to one-dimensional spaces by tracking filament bodies using SOAC and probabilistically estimating tip locations along the curve length of SOACs. Experimental evaluation on TIRFM image sequences with very low SNRs demonstrates the accuracy and robustness of this approach.

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“…Other methods of tracking filament tips employ Particle Filters (PF) [7], [8]. However, the two methods ignore the supporting information from filament bodies and may fail to locate a tip away from the filament body.…”

Section: Introductionmentioning

confidence: 99%

Actin Filament Segmentation Using Spatiotemporal Active-Surface and Active-Contour Models

Shen

Vavylonis

et al. 2010

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010

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We introduce a novel algorithm for actin filament segmentation in a 2D TIRFM image sequence. We treat the 2D time-lapse sequence as a 3D image volume and propose an over-grown active surface model to segment the body of a filament on all slices simultaneously. In order to locate the two ends of the filament on the over-grown surface, a novel 2D spatiotemporal domain is created based on the resulting surface. Two 2D active contour models deform in this domain to locate the two filament ends accurately. Evaluation on TIRFM image sequences with very low SNRs and comparison with a previous method demonstrate the accuracy and robustness of this approach.

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Using Particle Filter to Track and Model Microtubule Dynamics

Cited by 9 publications

References 9 publications

Automatic tip selection for microtubule dynamics quantification

Automatic tip selection for microtubule dynamics quantification

Actin Filament Tracking Based on Particle Filters and Stretching Open Active Contour Models

Actin Filament Segmentation Using Spatiotemporal Active-Surface and Active-Contour Models

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