Signal Processing Challenges in Quantitative 3-D Cell Morphology: More than meets the eye

Dufour, Alexandre; Liu, Tzu-Yu; Ducroz, Christel; Tournemenne, Robin; Cummings, Beryl; Thibeaux, Roman; Guillén, Nancy; Hero, Alfred O.; Olivo‐Marin, Jean‐Christophe

doi:10.1109/msp.2014.2359131

Cited by 37 publications

(38 citation statements)

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“…3) Discussion and Perspectives: A number of pioneering studies in cell shape classification [278]- [281] based on the previous approaches have opened up the way to start and connect phenomenological features like the structure of blebs, the dynamics of the subcortical cytoskeleton and its links to membrane during protrusion to the underlying molecular mechanisms that determine and regulate them. They represent also a first step towards understanding how cells generate force for shape change and movement and how they respond to mechanical force stimuli.…”

Section: ) Cell Tracking Methodsmentioning

confidence: 99%

A Guided Tour of Selected Image Processing and Analysis Methods for Fluorescence and Electron Microscopy

Kervrann

Sorzano

Acton

et al. 2016

IEEE J. Sel. Top. Signal Process.

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Abstract-Microscopy imaging, including fluorescence microscopy and electron microscopy, has taken a prominent role in life science research and medicine due to its ability to investigate the 3D interior of live cells and organisms. A long-term research in bio-imaging at the sub-cellular and cellular scales consists then in inferring the relationships between the dynamics of macromolecules and their functions. In this area, image processing and analysis methods are now essential to understand the dynamic organization of groups of interacting molecules inside molecular machineries and to address issues in fundamental biology driven by advances in molecular biology, optics and technology. In this paper, we present recent advances in fluorescence and electron microscopy and we focus on dedicated image processing and analysis methods required to quantify phenotypes for a limited number but typical studies in cell imaging.

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Section: ) Cell Tracking Methodsmentioning

confidence: 99%

A Guided Tour of Selected Image Processing and Analysis Methods for Fluorescence and Electron Microscopy

Kervrann

Sorzano

Acton

et al. 2016

IEEE J. Sel. Top. Signal Process.

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“…MM, a nonlinear analysis method based on signal processing in time domain, is excellent in noise removal and fast calculation [4]. MM is mainly applied to extract specific features in the neighborhood of every sample in the signal under analysis [5].…”

Section: Ntroductionmentioning

confidence: 99%

Power disturbance identification based on transient behaviors using morphological max-lifting scheme and nonlinear principal component analysis

Zhang

2015

2015 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA)

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When a power system is contaminated by power quality problems, a short transition will appear before power system restoring to a new stability state. The short transitions of various power disturbances correspond to different principal component characteristics. According to these characteristics, in this paper, a scheme based on transient behaviors using morphological max-lifting scheme (MMLS) and nonlinear principal component analysis (NLPCA) is developed for the identification of power disturbances. PSCAD/EMTDC is applied to simulate six types of power disturbances, and massive simulation work has been conducted to demonstrate the effectiveness and feasibility of the proposed scheme.Index Terms--Morphological max-lifting scheme; nonlinear principal component analysis; power disturbance; transient behavior; identification.

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“…While efforts have been made to develop cell and nuclear shape characteristics in 2D or pseudo-3D 11,12 , several studies have demonstrated that 3D morphometric measures provide better results for nuclear shape description and discrimination [13][14][15] . However, 3D shape descriptors that permit robust morphological analysis and facilitate human interpretation are still under active investigation 16 . Additionally, the dimensionality and volume of acquired data, various image acquisition conditions, and great variability of cell shapes in a population present challenges for 3D shape analysis methods that should be scalable, robust to noise, and specific enough across cell populations at the same time.…”

Section: Introduction Motivationmentioning

confidence: 99%

3D Shape Modeling for Cell Nuclear Morphological Analysis and Classification

Kalinin

Allyn-Feuer

Ade

et al. 2018

Preprint

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Quantitative analysis of morphological changes in a cell nucleus is important for understanding of nuclear architecture and their relationship with pathological conditions such as cancer.. CC-BY 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/313411 doi: bioRxiv preprint first posted online May. 3, 2018; 2 However, dimensionality of imaging data, together with a great variability of nuclear shapes present challenges for 3D morphological analysis. Thus, there is a compelling need for robust 3D nuclear morphometric techniques to carry out population-wise analysis. We propose a new approach that combines modeling, analysis, and interpretation of morphometric characteristics of cell nuclei and nucleoli in 3D. We use robust surface reconstruction that allows accurate approximation of 3D object boundary. Then, we compute geometric morphological measures characterizing the form of cell nuclei and nucleoli. Using these features, we compare over 450 nuclei with about 1,000 nucleoli of epithelial and mesenchymal prostate cancer cells, as well as 1,000 nuclei with over 2,000 nucleoli from serum-starved and proliferating fibroblast cells.Classification of sets of 9 and 15 cells achieves accuracy of 95.4% and 98%, respectively, for prostate cancer cells, and 95% and 98% for fibroblast cells. To our knowledge, this is the first attempt to combine these methods for 3D nuclear shape modeling and morphometry into a highly parallel pipeline workflow for morphometric analysis of thousands of nuclei and nucleoli in 3D.

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Signal Processing Challenges in Quantitative 3-D Cell Morphology: More than meets the eye

Cited by 37 publications

References 64 publications

A Guided Tour of Selected Image Processing and Analysis Methods for Fluorescence and Electron Microscopy

A Guided Tour of Selected Image Processing and Analysis Methods for Fluorescence and Electron Microscopy

Power disturbance identification based on transient behaviors using morphological max-lifting scheme and nonlinear principal component analysis

3D Shape Modeling for Cell Nuclear Morphological Analysis and Classification

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