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Pathological image segmentation for neuroblastoma using the GPU

A. Ruiz, Jun Kong, M. Ujaldon, K. Boyer, J. Saltz, M. Gurcan
Dept. of Computer Architecture, University of Malaga, Malaga, Spain
5th IEEE International Symposium on In Biomedical Imaging: From Nano to Macro, pp. 296-299, 2008

@conference{ruiz2008pathological,

   title={Pathological image segmentation for neuroblastoma using the GPU},

   author={Ruiz, A. and Kong, J. and Ujaldon, M. and Boyer, K. and Saltz, J. and Gurcan, M.},

   booktitle={Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on},

   pages={296–299},

   year={2008},

   organization={IEEE}

}

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We present a novel use of GPUs (graphics processing units) for the analysis of histopathological images of neuroblastoma, a childhood cancer. Thanks to the advent of modern microscopy scanners, whole-slide histopathological images can now be acquired but the computational costs to analyze these images using sophisticated image analysis algorithms are usually high. In this study, we have implemented previously developed image analysis algorithms using GPUs to exploit their outstanding processing power and memory bandwidth. The resulting GPU code was contrasted and combined with a C++ implementation on a multicore CPU to maximize parallelism on emerging architectures. Our codes were tested on different classes of images, with performance gain factors about 5.6x when the execution time of a Matlab code running on the CPU is compared with a code running jointly on CPU and GPU.
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