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

Knötel, D., Seidel, R., Prohaska, S., Dean, M. N., & Baum, D. (2017). Automated segmentation of complex patterns in biological tissues: Lessons from stingray tessellated cartilage. PLoS One, 12(12): e0188018. doi:10.1371/journal.pone.0188018.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0000-3AD7-D Version Permalink: http://hdl.handle.net/21.11116/0000-0000-3AD8-C
Genre: Journal Article

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 Creators:
Knötel, David, Author
Seidel, Ronald1, Author              
Prohaska, Steffen, Author
Dean, Mason N.2, Author              
Baum, Daniel, Author
Affiliations:
1Peter Fratzl, Biomaterialien, Max Planck Institute of Colloids and Interfaces, Max Planck Society, ou_1863294              
2Mason Dean (Indep. Res.), Max Planck Institute of Colloids and Interfaces, Max Planck Society, ou_2231639              

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Free keywords: Open Access
 Abstract: 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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 Dates: 2017-12
 Publication Status: Published in print
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 Identifiers: DOI: 10.1371/journal.pone.0188018
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Title: PLoS One
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 12 (12) Sequence Number: e0188018 Start / End Page: - Identifier: ISSN: 1932-6203