Unlike most other vertebrate animals, sharks and rays have skeletons made entirely of cartilage and not bone. In contrast to the cartilage in our knees, however, a shark’s skeleton is “tessellated”: skeletal elements made of uncalcified cartilage are wrapped in an outer layer of thousands of mineralized tiles called tesserae. Although this character has defined the shark and ray lineage for hundreds of millions of years, it is still unclear what role skeletal tiling plays in the mechanics and growth of this ancient, composite skeletal material.

The HFSP-funded project aims to describe the tiling morphologies of shark and ray cartilaginous skeletons, to understand their importance to the mechanical behavior of the skeleton. Through several collaborations, we use an integrative and iterative approach of morphological analyses, materials and mechanical testing, and modeling (physical, digital and theoretical). The subproject at ZIB focuses on the analysis of µCT data of shark and ray skeletons. In particular, the aim is to develop tools for flexible segmentations of the tessellated network, allowing the first 3d quantifications and visualizations of these tiling patterns and allowing comparison among species and developmental stages, but also with idealized tiled surfaces.

First Results

We have developed a workflow for the segmentation of µCT data sets of ray tesserae. Since the scalar value distribution of the tesserae does not enable a separation of the individual tesserae, we are using the following multi-step approach:

  1. Binary segmentation of tesseral layer
  2. Computation of distance transform of the tesseral layer
  3. Segmentation of individual tesserae based on distance map
  4. Computation of graph representation of tesserae network
  5. Interactive correction of segmentation using graph representation

Segmentation Workflow

Segmentation workflow (from left to right and top to bottom): Volume rendering of original data; surface of binary segmentation; volume rendering of distance field; initial segmentation using distance field; graph representation; final segmentation.

Using the described workflow, we were able to segment the data set shown in the teaser image in approximately one hour. This data set contains a few hundred tesserae. It is infeasible to segment the data manually and, hence, automated methods are a necessity. During the continuation, we will work on further improving the automatic segmentation both in terms of quality and user interaction time.