There has been a debate on whether to use 2D or 3D deep neural networks for
volumetric organ segmentation. Both 2D and 3D models have their advantages and
disadvantages. In this paper, we present an alternative framework, which trains
2D networks on different viewpoints for segmentation, and builds a 3D
Volumetric Fusion Net (VFN) to fuse the 2D segmentation results. VFN is
relatively shallow and contains much fewer parameters than most 3D networks,
making our framework more efficient at integrating 3D information for
segmentation. We train and test the segmentation and fusion modules
individually, and propose a novel strategy, named cross-cross-augmentation, to
make full use of the limited training data. We evaluate our framework on
several challenging abdominal organs, and verify its superiority in
segmentation accuracy and stability over existing 2D and 3D approaches.

本文提出了一种新颖的方法，利用 2D 网络对不同视角进行器官分割训练，并构建一个 3D 立体融合网络（VFN）来融合 2D 分割结果，VFN 相对较浅，包含的参数比大多数 3D 网络少，从而使得我们的框架更加高效，我们提出了一个名为交叉增强的新颖策略，最终在多个具有挑战性的腹部器官上验证了我们的框架在分割准确性和稳定性上优于现有的 2D 和 3D 方法。