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A curvature-enhanced random walker segmentation method for detailed capture of 3D cell surface Membranes
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Lutton, Judith E., Collier, Sharon and Bretschneider, Till (2021) A curvature-enhanced random walker segmentation method for detailed capture of 3D cell surface Membranes. IEEE Transactions on Medical Imaging, 40 (2). pp. 514-526. doi:10.1109/TMI.2020.3031029 ISSN 0278-0062.
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WRAP-curvature-enhanced-random-walker-segmentation-3D-cell-surface-membranes-VoR-Bretschneider-2020.pd.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (2784Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/TMI.2020.3031029
Abstract
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 3D images of Dictyostelium cells, and performs competitively against CNN-based methods in two Cell Tracking Challenge datasets, demonstrating the ability to obtain accurate segmentations without the requirement of large training datasets. We also present an automated seeding method for microscopy data, which, combined with the curvature-enhanced random walker method, enables the segmentation of large time series with minimal input from the experimenter.
Item Type: | Journal Article | |||||||||
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Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QH Natural history T Technology > TA Engineering (General). Civil engineering (General) |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||
Library of Congress Subject Headings (LCSH): | Cell membranes , Cell separation -- Simulation methods, Three-dimensional imaging , Image processing—Digital techniques | |||||||||
Journal or Publication Title: | IEEE Transactions on Medical Imaging | |||||||||
Publisher: | IEEE | |||||||||
ISSN: | 0278-0062 | |||||||||
Official Date: | February 2021 | |||||||||
Dates: |
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Volume: | 40 | |||||||||
Number: | 2 | |||||||||
Page Range: | pp. 514-526 | |||||||||
DOI: | 10.1109/TMI.2020.3031029 | |||||||||
Status: | Peer Reviewed | |||||||||
Publication Status: | Published | |||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||
Date of first compliant deposit: | 26 October 2020 | |||||||||
Date of first compliant Open Access: | 28 October 2020 | |||||||||
RIOXX Funder/Project Grant: |
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