Automatic detection of 3D cell protrusions using spherical wavelets

Ducroz, Christel; Olivo‐Marin, Jean‐Christophe; Dufour, Alexandre

doi:10.1109/icip.2013.6738722

Cited by 4 publications

References 19 publications

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A Curvature-Enhanced Random Walker Segmentation Method for Detailed Capture of 3D Cell Surface Membranes

Lutton

Collier

Bretschneider

2019

Preprint

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High-resolution 3D microscopy is a fast advancing field and requires new techniques in image analysis to handle these new datasets. In this work, we focus on detailed 3D segmentation of Dictyostelium cells undergoing macropinocytosis captured on an iSPIM microscope. We propose a novel random walker-based method with a curvature-based enhancement term, with the aim of capturing fine protrusions, such as filopodia and deep invaginations, such as macropinocytotic cups, on the cell surface. We tested our method on both real and synthetic 3D image volumes, demonstrating that the inclusion of the curvature enhancement term can improve the segmentation of the aforementioned features. We show that our method performs better than other state of the art segmentation methods in real microscopy data, and performs better or similar to these methods in synthetic data. We also present an automated seeding method for microscopy data, which, combined with the curvature-enhanced random walker method, enables the rapid segmentation of large time series with minimal input from the experimenter.

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A Curvature-Enhanced Random Walker Segmentation Method for Detailed Capture of 3D Cell Surface Membranes

Lutton

Collier

Bretschneider

2019

Preprint

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Stromule branch tip detection based on accurate cell image segmentation

Ren

Caplan

et al. 2017

2017 IEEE International Conference on Image Processing (ICIP)

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

Dufour

Liu

Ducroz

et al. 2015

IEEE Signal Process. Mag.

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International audienceModern developments in light microscopy have allowed the observation of cell deformation with remarkable spatiotemporal resolution and reproducibility. Analyzing such phenomena is of particular interest for the signal processing and computer vision communities due to the numerous computational challenges involved, from image acquisition all the way to shape analysis and pattern recognition and interpretation. This article aims at providing an up-to-date overview of the problems, solutions, and remaining challenges in deciphering the morphology of living cells via computerized approaches, with a particular focus on shape description frameworks and their exploitation using machine-learning techniques. As a concrete illustration, we use our recently acquired data on amoeboid cell deformation, motivated by its direct implication in immune responses, bacterial invasion, and cancer metastasis

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Automatic detection of 3D cell protrusions using spherical wavelets

Cited by 4 publications

References 19 publications

A Curvature-Enhanced Random Walker Segmentation Method for Detailed Capture of 3D Cell Surface Membranes

A Curvature-Enhanced Random Walker Segmentation Method for Detailed Capture of 3D Cell Surface Membranes

Stromule branch tip detection based on accurate cell image segmentation

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

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