Bone Segmentation Workflow for Murine Hindpaw
Semi-automatic workflow and recipe for high-throughput bone segmentation of murine hindpaw micro-CT datasets.
Segmenting and separating individual bones of the mouse paw in micro-CT datasets is challenging and tedious. Approaches based on watershed are efficient, but still require a single marker per bone. This Xtra provides an automatic marker generation workflow: it provides an automatic method for generating such markers, as well as an efficient manual procedure for correcting errors and reconstructing the final segmentation.
This Xtra includes two example datasets, an automatic recipe, and a description of the interactive workflow detailed in the following publication and its supplementary material:
H. Mark Kenney, Yue Peng, Kiana L. Chen, Raquel Ajalik, Lindsay Schnur, Ronald W. Wood, Edward M. Schwarz, Hani A. Awad, A high-throughput semi-automated bone segmentation workflow for murine hindpaw micro-CT datasets,
Bone Reports, Vol. 16, 2022.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8816671/ (https://doi.org/10.1016/j.bonr.2022.101167)
Based on discussions between the Thermo Scientific Amira Software team,
H. Mark Kenney and Ronald W. Wood from the University of Rochester
School of Medicine and Dentistry, this Xtra seeks to facilitate adoption
of a workflow likely to enhance translational research programs.
The datasets distributed within this Xtra are courtesy of the authors of the publication, and for the educational purpose presented here.
Publications based on the ideas presented in this Xtra must cite the above-mentioned reference.
Contact the corresponding author for more information about usage.
Mark Kenney, University of Rochester Medical Center