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Development of automated flexible thresholding algorithm for micro-CT scans of 3D bioprinted bone organoids
3D-bioprinted bone scaffolds show vast potential by enabling bone mineralization in vitro. The progress of bone mineralization is assessed by micro-CT imaging followed by image processing. We want to improve the detection of mineralized bone to obtain a more accurate scaffold structure.
Keywords: computational, scaffold, bone, engineering, micro-CT, in vitro, python, local, algorithm, medical engineering, image processing, programming
Engineered scaffolds with appropriate mechanical properties for bone healing are sought to treat challenging fractures. They also pose a new opportunity to investigate bone biology and mechanics by creating in vitro bone organoids for research. Cell-seeded scaffolds are 3D-bioprinted and then cultured over multiple weeks to investigate bone tissue mineralization. Using micro computed Tomography (micro-CT), scaffolds are scanned weekly, to assess the process and progress of mineralization in the scaffolds.
Currently, a single threshold at a given density is used to decide, which part of the micro-CT image can be considered as mineral and which as background. This results low mineralized parts of the scaffold being labelled as background and vice versa, as the mineral density can be quite low and the imaging process can lead to different background distributions.
Engineered scaffolds with appropriate mechanical properties for bone healing are sought to treat challenging fractures. They also pose a new opportunity to investigate bone biology and mechanics by creating in vitro bone organoids for research. Cell-seeded scaffolds are 3D-bioprinted and then cultured over multiple weeks to investigate bone tissue mineralization. Using micro computed Tomography (micro-CT), scaffolds are scanned weekly, to assess the process and progress of mineralization in the scaffolds. Currently, a single threshold at a given density is used to decide, which part of the micro-CT image can be considered as mineral and which as background. This results low mineralized parts of the scaffold being labelled as background and vice versa, as the mineral density can be quite low and the imaging process can lead to different background distributions.
The goal of this project is to develop a more flexible automated approach to distinguish mineralized tissue from background, based on the density distributions of scaffold and background. The scope can be adjusted to the type of project
The goal of this project is to develop a more flexible automated approach to distinguish mineralized tissue from background, based on the density distributions of scaffold and background. The scope can be adjusted to the type of project
Programming skills would be helpful, but are not necessary.
If you're interested, motivated and like problem-solving, please write me an email!
Julia Griesbach
julia.griesbach@hest.ethz.ch
Programming skills would be helpful, but are not necessary. If you're interested, motivated and like problem-solving, please write me an email! Julia Griesbach julia.griesbach@hest.ethz.ch