DOI: 10.5061/dryad.f53s5
title: Data from: Automated segmentation of complex patterns in biological tissues: lessons from stingray tessellated cartilage

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.
description: Supplementary-Material_Tesserae-SegmentationSupplementary data to reproduce and understand key results from the related publication, including original image data and processed data. In particular, sections from hyomandibulae harvested from specimens of round stingray Urobatis halleri, donated from another study (DOI: 10.1002/etc.2564). Specimens were from sub-adults/adults collected by beach seine from collection sites in San Diego and Seal Beach, California, USA. The hyomandibulae were mounted in clay, sealed in ethanol-humidified plastic tubes and scanned with a Skyscan 1172 desktop μCT scanner (Bruker μCT, Kontich, Belgium) in association with another study (DOI: 10.1111/joa.12508). Scans for all samples were performed with voxel sizes of 4.89 μm at 59 kV source voltage and 167 μA source current, over 360◦ sample 120 rotation. For our segmentations, the datasets were resampled to a voxel size of 9.78 μm to reduce the size of the images and speed up processing. In addition, the processed data that was generated with the visualization software Amira with techniques described in the related publication based on the mentioned specimens.