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SurfaceNets for Multi-Label Segmentations with Preservation of Sharp Boundaries

We extend 3D SurfaceNets to generate surfaces of segmented 3D medical images composed of multiple materials represented as indexed labels. Our extension generates smooth, high-quality triangle meshes suitable for rendering and tetrahedralization, preserves topology and sharp boundaries between mater...

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Detalles Bibliográficos
Autor principal: Frisken, Sarah F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9623606/
https://www.ncbi.nlm.nih.gov/pubmed/36325473
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author Frisken, Sarah F.
author_facet Frisken, Sarah F.
author_sort Frisken, Sarah F.
collection PubMed
description We extend 3D SurfaceNets to generate surfaces of segmented 3D medical images composed of multiple materials represented as indexed labels. Our extension generates smooth, high-quality triangle meshes suitable for rendering and tetrahedralization, preserves topology and sharp boundaries between materials, guarantees a user-specified accuracy, and is fast enough that users can interactively explore the trade-off between accuracy and surface smoothness. We provide open-source code in the form of an extendable C++ library with a simple API, and a Qt and OpenGL-based application that allows users to import or randomly generate multi-label volumes to experiment with surface fairing parameters. In this paper, we describe the basic SurfaceNets algorithm, our extension to handle multiple materials, our method for preserving sharp boundaries between materials, and implementation details used to achieve efficient processing.
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spelling pubmed-96236062022-11-01 SurfaceNets for Multi-Label Segmentations with Preservation of Sharp Boundaries Frisken, Sarah F. J Comput Graph Tech Article We extend 3D SurfaceNets to generate surfaces of segmented 3D medical images composed of multiple materials represented as indexed labels. Our extension generates smooth, high-quality triangle meshes suitable for rendering and tetrahedralization, preserves topology and sharp boundaries between materials, guarantees a user-specified accuracy, and is fast enough that users can interactively explore the trade-off between accuracy and surface smoothness. We provide open-source code in the form of an extendable C++ library with a simple API, and a Qt and OpenGL-based application that allows users to import or randomly generate multi-label volumes to experiment with surface fairing parameters. In this paper, we describe the basic SurfaceNets algorithm, our extension to handle multiple materials, our method for preserving sharp boundaries between materials, and implementation details used to achieve efficient processing. 2022 2022-02-28 /pmc/articles/PMC9623606/ /pubmed/36325473 Text en https://creativecommons.org/licenses/by-nd/3.0/The Authors provide this document (the Work) under the Creative Commons CC BY-ND 3.0 license available online at http://creativecommons.org/licenses/by-nd/3.0/ (https://creativecommons.org/licenses/by-nd/3.0/) . The Authors further grant permission for reuse of images and text from the first page of the Work, provided that the reuse is for the purpose of promoting and/or summarizing the Work in scholarly venues and that any reuse is accompanied by a scientific citation to the Work.
spellingShingle Article
Frisken, Sarah F.
SurfaceNets for Multi-Label Segmentations with Preservation of Sharp Boundaries
title SurfaceNets for Multi-Label Segmentations with Preservation of Sharp Boundaries
title_full SurfaceNets for Multi-Label Segmentations with Preservation of Sharp Boundaries
title_fullStr SurfaceNets for Multi-Label Segmentations with Preservation of Sharp Boundaries
title_full_unstemmed SurfaceNets for Multi-Label Segmentations with Preservation of Sharp Boundaries
title_short SurfaceNets for Multi-Label Segmentations with Preservation of Sharp Boundaries
title_sort surfacenets for multi-label segmentations with preservation of sharp boundaries
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9623606/
https://www.ncbi.nlm.nih.gov/pubmed/36325473
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