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Proceedings ArticleDOI

Semi-automated tracking of muscle satellite cells in brightfield microscopy video

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TLDR
A semi-automated approach for satellite cell tracking on myofibers consisting of registration with illumination correction, background subtraction and particle filtering is proposed and initial experimental results show the effectiveness of the approach.
Abstract
Muscle satellite cells, also known as myogenic precursor cells, are the dedicated stem cells responsible for postnatal skeletal muscle growth, repair, and hypertrophy. Biological studies aimed at describing satellite cell activity on their host myofiber using timelapse light microscopy enable qualitative study, but high-throughput automatic tracking of satellite cells translocating on myofibers is very difficult due to their complex motion across the three-dimensional surface of myofibers and the lack of discriminating cell features. Other complicating factors include inhomogeneous illumination, fixed focal plane, low contrast, and stage motion. We propose a semi-automated approach for satellite cell tracking on myofibers consisting of registration with illumination correction, background subtraction and particle filtering. Initial experimental results show the effectiveness of the approach.

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Citations
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Journal ArticleDOI

Mitosis Detection for Invasive Breast Cancer Grading in Histopathological Images

TL;DR: A fast and accurate approach for automatic mitosis detection from histopathological images is proposed by restricting the scales with the maximization of relative-entropy between the cells and the background to result in precise cell segmentation.
Proceedings ArticleDOI

Enhanced Random Forest for Mitosis Detection

TL;DR: This work proposes a fast and accurate approach for automatic mitosis detection from histopathological images using an enhanced random forest classifier with weighted random trees.
Book ChapterDOI

Methods for Observing and Quantifying Muscle Satellite Cell Motility and Invasion In Vitro.

TL;DR: Three protocols developed in the group for quantitatively analyzing satellite cell motility over time are described, which allow identification and longitudinal evaluation of individual cells over time and quantification of variations in motility due to intrinsic or extrinsic factors.

Computer Vision Based Tracking Of Biological Cells-A

TL;DR: This article surveys the recent literature in the area of computer vision based automated cell tracking and discusses the latest trends and successes in the development and introduction of automated celltracking techniques and systems.
References
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Journal ArticleDOI

Sequential Monte Carlo smoothing with application to parameter estimation in non-linear state space models

TL;DR: In this article, a modified version of the standard SMC technique is proposed for smoothing in general state space models, which relies on forgetting properties of the filtering dynamics and the quality of the estimates produced.
Journal ArticleDOI

Tracking cells in Life Cell Imaging videos using topological alignments

TL;DR: Practical experiments demonstrate that the matching task can be solved efficiently in practice, and that the method is both effective and useful for tracking cells in data sets derived from a so-called Large Scale Digital Cell Analysis System (LSDCAS).
Proceedings ArticleDOI

Cell Tracking in Video Microscopy Using Bipartite Graph Matching

TL;DR: This paper model the problem of cell tracking over pairs of video microscopy image frames as a minimum weight matching problem in bipartite graphs and proposes two different tracking methods based on bipartites graph matching and properties of Gaussian distributions.
Journal ArticleDOI

Auto-validating von Neumann rejection sampling from small phylogenetic tree spaces.

TL;DR: In this article, an auto-validating version of the rejection sampler, via interval analysis, is introduced to rigorously draw samples from posterior distributions over small phylogenetic tree spaces.
Posted Content

Auto-validating von Neumann Rejection Sampling from Small Phylogenetic Tree Spaces

TL;DR: An auto-validating version of the rejection sampler due to von Neumann is introduced, via interval analysis, to rigorously draw samples from posterior distributions over small phylogenetic tree spaces, solving the open problem of rigorously drawing independent and identically distributed samples from the posterior distribution over rooted and unrooted small tree spaces.