  Image segmentation plays an important role in computer vision, object
detection, traffic control, and video surveillance. Typically, it is a critical
step in the 3D reconstruction of a specific organ in medical image processing
which unveils the detailed tomography of organ, tumor, and nerve, and thus
helping to improve the quality of surgical pathology. However, there may be
high computational requirements in it. With the advent of GPUs, more complex
and realistic models can be simulated, but the deployment of these facilities
also requires a huge amount of capital. As a consequence, how to make good use
of these computational resource is essential to GPU computing. This study
discusses the image segmentation of 3D airway reconstruction, identifies the
computing-intensive task, and parallelizes the algorithm of image segmentation
in order to obtain theoretical maximum speedup in terms of the benchmark ratio
of GPU to CPU. There are five steps involved, which are the image acquisition,
pre-processing, segmentation, reconstruction, and object recognition. It is
worth to note that it takes 85\% of time on segmentation. This study
successfully accelerates the image segmentation of 3D airway reconstruction by
optimizing the memory usage, grid and block setting and multiple GPUs
communication, thereby gaining a total speedup of 61.8 on two GPUs (Nvidia
K40).
