  In recent years, 3D convolutional neural networks have become the dominant
approach for volumetric medical image segmentation. However, compared to their
2D counterparts, 3D networks introduce substantially more training parameters
and higher requirement for the GPU memory. This has become a major limiting
factor for designing and training 3D networks for high-resolution volumetric
images. In this work, we propose a novel memory-efficient network architecture
for 3D high-resolution image segmentation. The network incorporates both global
and local features via a two-stage U-net-based cascaded framework and at the
first stage, a memory-efficient U-net (meU-net) is developed. The features
learnt at the two stages are connected via post-concatenation, which further
improves the information flow. The proposed segmentation method is evaluated on
an ultra high-resolution microCT dataset with typically 250 million voxels per
volume. Experiments show that it outperforms state-of-the-art 3D segmentation
methods in terms of both segmentation accuracy and memory efficiency.
