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Quantitative Analysis of Osteocyte Lacunae
State of the art microCT has allowed researchers to visualise the cell containing voids in the bone, know as lacunae. However analysis of such images is complicated by artefacts caused by physical limitations in the technology. This project aims to develop algorithms to correct for such artefacts.
Bone is a dynamic material, capable of adjusting its form according to use and of repairing itself in the case of injury. These remarkable abilities arise from specialised cells capable of depositing and removing material, and most importantly their coordinated action arises from a network of mechanically sensitive cells (osteocytes) embedded in the bone tissue in voids known as lacunae. Understanding how this network develops and functions is critical for developing new drugs and therapies to improve bone health.
Currently synchrotron-CT is the gold standard in visualisation of the lacunar network in meaningfully quantities of bone; however this is extremely limited in availability due to the high-cost of using the instrument and restricted beam time; few studies have assessed the lacunae and their network in three-dimensional space. Desktop micro-computed tomography is widely available to the scientific community and is a promising alternative, allowing the acquisition of images with sufficient spatial resolution to resolve individual lacunae. However, the poly-chromatic x-ray source produces artefacts within the image which do not exist in synchrotron-CT, making global segmentation of the lacunae difficult.
Task:
100% programming
Bone is a dynamic material, capable of adjusting its form according to use and of repairing itself in the case of injury. These remarkable abilities arise from specialised cells capable of depositing and removing material, and most importantly their coordinated action arises from a network of mechanically sensitive cells (osteocytes) embedded in the bone tissue in voids known as lacunae. Understanding how this network develops and functions is critical for developing new drugs and therapies to improve bone health.
Currently synchrotron-CT is the gold standard in visualisation of the lacunar network in meaningfully quantities of bone; however this is extremely limited in availability due to the high-cost of using the instrument and restricted beam time; few studies have assessed the lacunae and their network in three-dimensional space. Desktop micro-computed tomography is widely available to the scientific community and is a promising alternative, allowing the acquisition of images with sufficient spatial resolution to resolve individual lacunae. However, the poly-chromatic x-ray source produces artefacts within the image which do not exist in synchrotron-CT, making global segmentation of the lacunae difficult.
Task: 100% programming
The aim of this project is to develop an image segmentation algorithm capable of locally segmenting lacunae and compensating for the artefacts when calculating their shape and volume. Once this algorithm has been validated it will then be applied to search for differences in bone which have undergone different therapies.
The aim of this project is to develop an image segmentation algorithm capable of locally segmenting lacunae and compensating for the artefacts when calculating their shape and volume. Once this algorithm has been validated it will then be applied to search for differences in bone which have undergone different therapies.
Contact: Duncan Betts, dbetts@ethz.ch / Institute for Biomechanics, ETH Zürich /
Professorship Ralph Müller
Contact: Duncan Betts, dbetts@ethz.ch / Institute for Biomechanics, ETH Zürich / Professorship Ralph Müller