Contextual Embedding Learning to Enhance 2D Networks for Volumetric
  Image Segmentation
Zhuoyuan Wang Dong Sun Xiangyun Zeng Ruodai Wu Yi Wang
The segmentation of organs in volumetric medical images plays an important
role in computer-aided diagnosis and treatment/surgery planning. Conventional
2D convolutional neural networks (CNNs) can hardly exploit the spatial
correlation of volumetric data. Current 3D CNNs have the advantage to extract
more powerful volumetric representations but they usually suffer from occupying
excessive memory and computation nevertheless. In this study we aim to enhance
the 2D networks with contextual information for better volumetric image
segmentation. Accordingly, we propose a contextual embedding learning approach
to facilitate 2D CNNs capturing spatial information properly. Our approach
leverages the learned embedding and the slice-wisely neighboring matching as a
soft cue to guide the network. In such a way, the contextual information can be
transferred slice-by-slice thus boosting the volumetric representation of the
network. Experiments on challenging prostate MRI dataset (PROMISE12) and
abdominal CT dataset (CHAOS) show that our contextual embedding learning can
effectively leverage the inter-slice context and improve segmentation
performance. The proposed approach is a plug-and-play, and memory-efficient
solution to enhance the 2D networks for volumetric segmentation. The code will
be publicly available.