3D level set method for blastomere segmentation of preimplantation embryos in fluorescence microscopy images

Grushnikov, Andrey; Niwayama, Ritsuya; Kanade, Takeo; Yagi, Yasushi

doi:10.1007/s00138-017-0880-0

Cited by 5 publications

(2 citation statements)

References 38 publications

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“…Moreover, the general level set form of the algorithm could be extended into a multi-phase version that could segment multiple regions [3]. Now, we are working on an adaptation of the proposed segmentation method into 3D [18,27]. We are also considering assessing the method on biomedical images and more experimental comparisons with very popular, fully convolutional approaches [29,54].…”

Section: Discussionmentioning

confidence: 99%

GPU-accelerated image segmentation based on level sets and multiple texture features

Reska

Krȩtowski

2020

Multimed Tools Appl

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In this paper, we present a fast multi-stage image segmentation method that incorporates texture analysis into a level set-based active contour framework. This approach allows integrating multiple feature extraction methods and is not tied to any specific texture descriptors. Prior knowledge of the image patterns is also not required. The method starts with an initial feature extraction and selection, then performs a fast level set-based evolution process and ends with a final refinement stage that integrates a region-based model. The presented implementation employs a set of features based on Grey Level Co-occurrence Matrices, Gabor filters and structure tensors. The high performance of feature extraction and contour evolution stages is achieved with GPU acceleration. The method is validated on synthetic and natural images and confronted with results of the most similar among the accessible algorithms.

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Section: Discussionmentioning

confidence: 99%

GPU-accelerated image segmentation based on level sets and multiple texture features

Reska

Krȩtowski

2020

Multimed Tools Appl

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“…Various techniques have been used in current cell segmentation pipelines ( Meijering, 2012 ), including deformable models such as level sets ( Grushnikov et al , 2018 ; Uba et al , 2020 ) and active meshes ( Dufour et al , 2011 ) along with machine learning approaches, such as pixel classification methods ( Hilsenbeck et al , 2017 ) and convolutional neural networks ( Cao et al , 2020 ; Supplementary Table S5 ), though the most widely used technique appears to still be the watershed algorithm ( Beucher, 1990 ; Stegmaier et al , 2016 ; Supplementary Table S5 ).…”

Section: Introductionmentioning

confidence: 99%

spheresDT/Mpacts-PiCS: cell tracking and shape retrieval in membrane-labeled embryos

et al. 2021

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Motivation Uncovering the cellular and mechanical processes that drive embryo formation requires an accurate read out of cell geometries over time. However, automated extraction of 3D cell shapes from time lapse microscopy remains challenging, especially when only membranes are labeled. Results We present an image analysis framework for automated tracking and three-dimensional cell segmentation in confocal time lapses. A sphere clustering approach allows for local thresholding and application of logical rules to facilitate tracking and unseeded segmentation of variable cell shapes. Next, the segmentation is refined by a discrete element method simulation where cell shapes are constrained by a biomechanical cell shape model. We apply the framework on C. elegans embryos in various stages of early development and analyse the geometry of the - and 8-cell stage embryo, looking at volume, contact area and shape over time. Availability The Python code for the algorithm and for measuring performance, along with all data needed to recreate the results is freely available at 10.5281/zenodo.5108416 and 10.5281/zenodo.4540092. The most recent version of the software is maintained at https://bitbucket.org/pgmsembryogenesis/sdt-pics Supplementary information Supplementary data are available at Bioinformatics online.

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Deep Semantic Edge for Cell Counting and Localization in Time-Lapse Microscopy Images

Zhang

Sun

2021

Pattern Recognition and Computer Vision

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3D level set method for blastomere segmentation of preimplantation embryos in fluorescence microscopy images

Cited by 5 publications

References 38 publications

GPU-accelerated image segmentation based on level sets and multiple texture features

GPU-accelerated image segmentation based on level sets and multiple texture features

spheresDT/Mpacts-PiCS: cell tracking and shape retrieval in membrane-labeled embryos

Deep Semantic Edge for Cell Counting and Localization in Time-Lapse Microscopy Images

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