Multi-GPU accelerated real-time retinal image segmentation

In recent years, Optical Coherence Tomography (OCT) has become one of the dominant imaging technologies for ophthalmic diagnostics and vision research. The fast and high-resolution cross-sectional data that OCT provides has brought a new possibility in the role of intra-operative imaging. However, existing commercial OCT systems lack the automated real-time functionality for providing immediate feedback of changes in anatomical configuration as the result of surgical actions. The predominant reason for lacking such functionality is because high complexity algorithms are hard to implement in real-time imaging due to their computationally expensive nature. In this thesis, we will present a Graphics Processing Unit (GPU) accelerated retinal layer segmentation for real-time intra-operative imaging applications. Modern GPUs has emerged as a strong tool for mass computation in scientific researches. The computational power of the GPU outpaces Central Processing Unit (CPU) significantly when the processing task is parallelizable. Image segmentation is a computationally expensive algorithm and traditionally implemented in sequential instructions. An example of a parallelizable segmentation algorithm is Push-Relabel (PR) Graph-Cut(GC), which can be implemented using GPU. The GPU Retinal Segmentation (GRS) presented in this thesis is built upon such an algorithm. To ensure the run time of the GRS meets the real-time requirement for its application, multiple GPUs are used to accelerate the segmentation processing further in parallel. As a result of using GRS, we were able to achieve the visualization of the retinal thickness measurement and the enhancement of retinal vasculature networks in real-time.