Region‐Based PDEs for Cells Counting and Segmentation in  3D+Time Images of Vertebrate Early Embryogenesis

Rizzi, B; Sarti, Alessandro

doi:10.1155/2009/968986

Cited by 8 publications

(8 citation statements)

References 26 publications

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“…To segment and track cells in 3D, a multilevel set method based on coupled active surfaces with or without edges was introduced (24). A regionbased PDE method was introduced to segment and count cells in 3D images of vertebrate embryogenesis (25). A fast level set method was applied to the segmentation of chromosomes in 2D time series images of early Drosophila embryos (26).…”

Section: Introductionmentioning

confidence: 99%

Three‐dimensional segmentation of nuclei and mitotic chromosomes for the study of cell divisions in live Drosophila embryos

Rambabu

Wasser

2011

Cytometry Pt A

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Drosophila embryogenesis is an established model to investigate mechanisms and genes related to cell divisions in an intact multicellular organism. Progression through the cell cycle phases can be monitored in vivo using fluorescently labeled fusion proteins and time-lapse microscopy. To measure cellular properties in microscopic images, accurate and fast image segmentation methods are a critical prerequisite. To quantify static and dynamic features of interphase nuclei and mitotic chromosomes, we developed a three-dimensional (3D) segmentation method based on multiple level sets. We tested our method on 3D time-series images of live embryos expressing histone-2Av-green fluorescence protein. Our method is robust to low signal-to-noise ratios inherent to high-speed imaging, fluorescent signals in the cytoplasm, and dynamic changes of shape and texture. Comparisons with manual ground-truth segmentations showed that our method achieves more than 90% accuracy on the object as well as voxel levels and performs consistently throughout all cell cycle phases and developmental stages from syncytial blastoderm to postblastoderm mitotic domains. ' 2011 International Society for Advancement of Cytometry

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Section: Introductionmentioning

confidence: 99%

Three‐dimensional segmentation of nuclei and mitotic chromosomes for the study of cell divisions in live Drosophila embryos

Rambabu

Wasser

2011

Cytometry Pt A

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“…A 3D level-set method was also applied to detect nuclei and mitotic chromosomes in Drosophila embryos (Chinta and Wasser, 2012). A region-based active contour method was developed in 3D cell counting and segmentation of vertebrae in early embryogenesis (Rizzi and Sarti, 2009). We applied a number of these methods to quantify nuclei number and shape in zebrafish, however we encountered a number of problems associated with imaging large amounts of nuclei in a relatively large sample.…”

Section: Introductionmentioning

confidence: 99%

WaveletSEG: Automatic wavelet-based 3D nuclei segmentation and analysis for multicellular embryo quantification

Wu¹,

Wang²,

Li³

et al. 2020

Preprint

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Identification of individual cells in tissues, organs, and in various developing systems is a well-studied problem because it is an essential part of objectively analyzing quantitative images in numerous biological contexts. We developed a size-dependent wavelet-based segmentation method that provides robust segmentation without any preprocessing, filtering or fine-tuning steps, and is robust to the signal-to-noise ratio (SNR). The wavelet-based method achieves robust segmentation results with respect to True Positive rate, Precision, and segmentation accuracy compared with other commonly used methods. We applied the segmentation program to zebrafish embryonic development IN TOTO for nuclei segmentation, image registration, and nuclei shape analysis. These new approaches to segmentation provide a means to carry out quantitative patterning analysis with single-cell precision throughout three dimensional tissues and embryos and they have a high tolerance for non-uniform and noisy image data sets.

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“…These algorithms can be classified into two classes with respect to the detection model being used. The first class of algorithms [14-18] performs frame-by-frame object detection, which do not use the information about the previous or next frames. The second class of algorithms [19-22] uses level set-based approaches for object detection in time-series images.…”

Section: Introductionmentioning

confidence: 99%

The study of muscle remodeling in Drosophila metamorphosis using in vivo microscopy and bioimage informatics

Rambabu¹,

Tan²,

Wasser³

2012

BMC Bioinformatics

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Background Metamorphosis in insects transforms the larval into an adult body plan and comprises the destruction and remodeling of larval and the generation of adult tissues. The remodeling of larval into adult muscles promises to be a genetic model for human atrophy since it is associated with dramatic alteration in cell size. Furthermore, muscle development is amenable to 3D in vivo microscopy at high cellular resolution. However, multi-dimensional image acquisition leads to sizeable amounts of data that demand novel approaches in image processing and analysis. Results To handle, visualize and quantify time-lapse datasets recorded in multiple locations, we designed a workflow comprising three major modules. First, the previously introduced TLM-converter concatenates stacks of single time-points. The second module, TLM-2D-Explorer, creates maximum intensity projections for rapid inspection and allows the temporal alignment of multiple datasets. The transition between prepupal and pupal stage serves as reference point to compare datasets of different genotypes or treatments. We demonstrate how the temporal alignment can reveal novel insights into the east gene which is involved in muscle remodeling. The third module, TLM-3D-Segmenter, performs semi-automated segmentation of selected muscle fibers over multiple frames. 3D image segmentation consists of 3 stages. First, the user places a seed into a muscle of a key frame and performs surface detection based on level-set evolution. Second, the surface is propagated to subsequent frames. Third, automated segmentation detects nuclei inside the muscle fiber. The detected surfaces can be used to visualize and quantify the dynamics of cellular remodeling. To estimate the accuracy of our segmentation method, we performed a comparison with a manually created ground truth. Key and predicted frames achieved a performance of 84% and 80%, respectively. Conclusions We describe an analysis pipeline for the efficient handling and analysis of time-series microscopy data that enhances productivity and facilitates the phenotypic characterization of genetic perturbations. Our methodology can easily be scaled up for genome-wide genetic screens using readily available resources for RNAi based gene silencing in Drosophila and other animal models.

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Region‐Based PDEs for Cells Counting and Segmentation in 3D+Time Images of Vertebrate Early Embryogenesis

Cited by 8 publications

References 26 publications

Three‐dimensional segmentation of nuclei and mitotic chromosomes for the study of cell divisions in live Drosophila embryos

Three‐dimensional segmentation of nuclei and mitotic chromosomes for the study of cell divisions in live Drosophila embryos

WaveletSEG: Automatic wavelet-based 3D nuclei segmentation and analysis for multicellular embryo quantification

The study of muscle remodeling in Drosophila metamorphosis using in vivo microscopy and bioimage informatics

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