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Automated segmentation of complex patterns in biological tissues: Lessons from stingray tessellated cartilage

INTRODUCTION: Many biological structures show recurring tiling patterns on one structural level or the other. Current image acquisition techniques are able to resolve those tiling patterns to allow quantitative analyses. The resulting image data, however, may contain an enormous number of elements....

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Autores principales: Knötel, David, Seidel, Ronald, Prohaska, Steffen, Dean, Mason N., Baum, Daniel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5728489/
https://www.ncbi.nlm.nih.gov/pubmed/29236705
http://dx.doi.org/10.1371/journal.pone.0188018
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author Knötel, David
Seidel, Ronald
Prohaska, Steffen
Dean, Mason N.
Baum, Daniel
author_facet Knötel, David
Seidel, Ronald
Prohaska, Steffen
Dean, Mason N.
Baum, Daniel
author_sort Knötel, David
collection PubMed
description INTRODUCTION: Many biological structures show recurring tiling patterns on one structural level or the other. Current image acquisition techniques are able to resolve those tiling patterns to allow quantitative analyses. The resulting image data, however, may contain an enormous number of elements. This renders manual image analysis infeasible, in particular when statistical analysis is to be conducted, requiring a larger number of image data to be analyzed. As a consequence, the analysis process needs to be automated to a large degree. In this paper, we describe a multi-step image segmentation pipeline for the automated segmentation of the calcified cartilage into individual tesserae from computed tomography images of skeletal elements of stingrays. METHODS: Besides applying state-of-the-art algorithms like anisotropic diffusion smoothing, local thresholding for foreground segmentation, distance map calculation, and hierarchical watershed, we exploit a graph-based representation for fast correction of the segmentation. In addition, we propose a new distance map that is computed only in the plane that locally best approximates the calcified cartilage. This distance map drastically improves the separation of individual tesserae. We apply our segmentation pipeline to hyomandibulae from three individuals of the round stingray (Urobatis halleri), varying both in age and size. RESULTS: Each of the hyomandibula datasets contains approximately 3000 tesserae. To evaluate the quality of the automated segmentation, four expert users manually generated ground truth segmentations of small parts of one hyomandibula. These ground truth segmentations allowed us to compare the segmentation quality w.r.t. individual tesserae. Additionally, to investigate the segmentation quality of whole skeletal elements, landmarks were manually placed on all tesserae and their positions were then compared to the segmented tesserae. With the proposed segmentation pipeline, we sped up the processing of a single skeletal element from days or weeks to a few hours.
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spelling pubmed-57284892017-12-22 Automated segmentation of complex patterns in biological tissues: Lessons from stingray tessellated cartilage Knötel, David Seidel, Ronald Prohaska, Steffen Dean, Mason N. Baum, Daniel PLoS One Research Article INTRODUCTION: Many biological structures show recurring tiling patterns on one structural level or the other. Current image acquisition techniques are able to resolve those tiling patterns to allow quantitative analyses. The resulting image data, however, may contain an enormous number of elements. This renders manual image analysis infeasible, in particular when statistical analysis is to be conducted, requiring a larger number of image data to be analyzed. As a consequence, the analysis process needs to be automated to a large degree. In this paper, we describe a multi-step image segmentation pipeline for the automated segmentation of the calcified cartilage into individual tesserae from computed tomography images of skeletal elements of stingrays. METHODS: Besides applying state-of-the-art algorithms like anisotropic diffusion smoothing, local thresholding for foreground segmentation, distance map calculation, and hierarchical watershed, we exploit a graph-based representation for fast correction of the segmentation. In addition, we propose a new distance map that is computed only in the plane that locally best approximates the calcified cartilage. This distance map drastically improves the separation of individual tesserae. We apply our segmentation pipeline to hyomandibulae from three individuals of the round stingray (Urobatis halleri), varying both in age and size. RESULTS: Each of the hyomandibula datasets contains approximately 3000 tesserae. To evaluate the quality of the automated segmentation, four expert users manually generated ground truth segmentations of small parts of one hyomandibula. These ground truth segmentations allowed us to compare the segmentation quality w.r.t. individual tesserae. Additionally, to investigate the segmentation quality of whole skeletal elements, landmarks were manually placed on all tesserae and their positions were then compared to the segmented tesserae. With the proposed segmentation pipeline, we sped up the processing of a single skeletal element from days or weeks to a few hours. Public Library of Science 2017-12-13 /pmc/articles/PMC5728489/ /pubmed/29236705 http://dx.doi.org/10.1371/journal.pone.0188018 Text en © 2017 Knötel et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Knötel, David
Seidel, Ronald
Prohaska, Steffen
Dean, Mason N.
Baum, Daniel
Automated segmentation of complex patterns in biological tissues: Lessons from stingray tessellated cartilage
title Automated segmentation of complex patterns in biological tissues: Lessons from stingray tessellated cartilage
title_full Automated segmentation of complex patterns in biological tissues: Lessons from stingray tessellated cartilage
title_fullStr Automated segmentation of complex patterns in biological tissues: Lessons from stingray tessellated cartilage
title_full_unstemmed Automated segmentation of complex patterns in biological tissues: Lessons from stingray tessellated cartilage
title_short Automated segmentation of complex patterns in biological tissues: Lessons from stingray tessellated cartilage
title_sort automated segmentation of complex patterns in biological tissues: lessons from stingray tessellated cartilage
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5728489/
https://www.ncbi.nlm.nih.gov/pubmed/29236705
http://dx.doi.org/10.1371/journal.pone.0188018
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