Paper Info
Title
High Resolution Medical Image Segmentation Using 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 training. However, the memory on a GPU is not large enough to train the models. A popular approach to tackling this problem is the patch-based method, which divides a large image into small patches and trains the models with these small patches. However, this method degrades 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 method, 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 enhanced the existing data-swapping method by introducing swapping inside forward propagation and selective swapping of analysis path in order to train 3D U-Net effectively. We applied this approach to train 3D U-Net with full-size images of 192 x 192 x 192 voxels for a brain tumor dataset. Compared with the patch-based method for patches of 128 x 128 x 128 voxels, our approach improved the mean Dice score by 3.9 percentage points and 4.1 percentage points when detecting a whole tumor sub-region and a tumor core sub-region, respectively. The total training time was reduced from 164 h to 47 h, resulting in an acceleration of 3.53 times.
Year
DOI
Venue
2019
10.1007/978-3-030-32248-9_27
Lecture Notes in Computer Science
Keywords
DocType
Volume
Deep learning,Image segmentation,3D U-Net,Data-swapping method
Conference
11766
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
haruki imai101.35
Samuel Matzek200.34
Tung D. Le322.08
Yasushi Negishi4366.36
Kawachiya, K.514516.81