Osteoporosis occurs when the body loses too much bone mass, and the bones become brittle and fragile. In the aging society of Europe, the number of people with osteoporosis is continuously growing. The disease not only severely impairs the life quality of the patients, but also causes a great burden to the healthcare system. To investigate on the disease mechanism and metabolism of the bones, X-ray microscopy scans of the mouse tibia are taken. As a fundamental step, the microstructures, such as the lacunae and vessels of the bones, need to be segmented and analyzed. With the recent advances in the deep learning technologies, segmentation networks with good performance have been proposed. However, these supervised deep nets are not directly applicable for the segmentation of these micro-structures, since manual annotations are not feasible due to the enormous data size. In this work, we propose a pipeline to model the mouse bone micro-structures. Our workflow integrates conventional algorithms with 3D modeling using Blender, and focuses on the anatomical micro-structures rather than the intensity distributions of the mouse bone scans. It provides the basis towards generating simulated mouse bone X-ray microscopy images, which could be used as the ground truth for training segmentation neural networks.
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- Towards Mouse Bone X-ray Microscopy Scan Simulation
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