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Using the GPU and Multi-Core CPU to Aid 3 D Structural Feature Extraction of Biological Tissue from Histology Slides

Extracting information about the structure of biological tissue from static image data is a complex task which requires a series of computationally intensive operations. Here we present how the power of multi-core CPUs and massively parallel GPUs have been utilised to extract information about the shape, size and path that the mammalian oviduct follows from histology images, and to create a realistic 3D environment. First, the histology images were processed to identify the mid-point and encapsulating elliptical region for all cross sections using a combination of GPU and CPU processing. These points were then connected together, resulting in a 3D curve following the path of the oviductal tube. This information was then related back to the histology images and the position along the tube of every cross section was identified from all images. Measurements were then taken from the cross sectional images, which were used to generate a series of 2D spline based cross sections for the length of the oviduct. The linear cross sections were then bound to the 3D path of the tube using a novel particle system based technique which provides smooth resolution of self intersections and crossovers from adjacent sections. This results in a unique 3D model of the oviduct, which as it is based on measurements of histology slides is grounded in reality. The GPU is used for processor intensive operations of image processing and particle physics simulation, significantly reducing the time required to generate a complete model. A set of models created using this technique are being used to investigate the influence that the 3D structure of the oviductal environment has on sperm transport and navigation. Keywords-CUDA; image processing; particle physics simulation; histology; 3D model;