Title: Fast and Accurate 3D Medical Image Segmentation with Data-swapping Method,
Abstract: Deep neural network models used for medical image segmentation are large
because they are trained with high-resolution three-dimensional (3D) images.
Graphics processing units (GPUs) are widely used to accelerate the trainings.
However, the memory on a GPU is not large enough to train the models. A popular
approach to tackling this problem is patch-based method, which divides a large
image into small patches and trains the models with these small patches.
However, this method would degrade the segmentation quality if a target object
spans multiple patches. In this paper, we propose a novel approach for 3D
medical image segmentation that utilizes the data-swapping, which swaps out
intermediate data from GPU memory to CPU memory to enlarge the effective GPU
memory size, for training high-resolution 3D medical images without patching.
We carefully tuned parameters in the data-swapping method to obtain the best
training performance for 3D U-Net, a widely used deep neural network model for
medical image segmentation. We applied our tuning to train 3D U-Net with
full-size images of 192 x 192 x 192 voxels in brain tumor dataset. As a result,
communication overhead, which is the most important issue, was reduced by
17.1%. Compared with the patch-based method for patches of 128 x 128 x 128
voxels, our training for full-size images achieved improvement on the mean Dice
score by 4.48% and 5.32 % for detecting whole tumor sub-region and tumor core
sub-region, respectively. The total training time was reduced from 164 hours to
47 hours, resulting in 3.53 times of acceleration.