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SplineDist: Automated Cell Segmentation with Spline Curves

Soham Mandal, Virginie Uhlmann
doi: https://doi.org/10.1101/2020.10.27.357640
Soham Mandal
European Bioinformatics Institute, European Molecular Biology Laboratory, Cambridge, UK
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Virginie Uhlmann
European Bioinformatics Institute, European Molecular Biology Laboratory, Cambridge, UK
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  • For correspondence: uhlmann@ebi.ac.uk
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ABSTRACT

We present SplineDist, an instance segmentation convolutional neural network for bioimages extending the popular StarDist method. While StarDist describes objects as star-convex polygons, SplineDist uses a more flexible and general representation by modelling objects as planar parametric spline curves. Based on a new loss formulation that exploits the properties of spline constructions, we can incorporate our new object model in StarDist’s architecture with minimal changes. We demonstrate in synthetic and real images that SplineDist produces segmentation outlines of equal quality than StarDist with smaller network size and accurately captures non-star-convex objects that cannot be segmented with StarDist.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted October 28, 2020.
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SplineDist: Automated Cell Segmentation with Spline Curves
Soham Mandal, Virginie Uhlmann
bioRxiv 2020.10.27.357640; doi: https://doi.org/10.1101/2020.10.27.357640
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SplineDist: Automated Cell Segmentation with Spline Curves
Soham Mandal, Virginie Uhlmann
bioRxiv 2020.10.27.357640; doi: https://doi.org/10.1101/2020.10.27.357640

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