Constructing Complex 3D Biological Environments from Medical Imaging Using High Performance Computing
Extracting information about the structure of biological tissue from static image data is a complex task requiring computationally intensive operations. Here, we present how multicore CPUs and GPUs have been utilized to extract information about the shape, size, and path followed by the mammalian oviduct, called the fallopian tube in humans, from histology images, to create a unique but realistic 3D virtual organ. Histology images were processed to identify the individual cross sections and determine the 3D path that the tube follows through the tissue. This information was then related back to the histology images, linking the 2D cross sections with their corresponding 3D position along the oviduct. A series of linear 2D spline cross sections, which were computationally generated for the length of the oviduct, were bound to the 3D path of the tube using a novel particle system technique that provides smooth resolution of self-intersections. This results in a unique 3D model of the oviduct, which is grounded in reality. The GPU is used for the processor intensive operations of image processing and particle physics based simulations, significantly reducing the time required to generate a complete model.
biological tissue gpu histology particle system image processing geometric reconstruction