  Recently 3D volumetric organ segmentation attracts much research interest in
medical image analysis due to its significance in computer aided diagnosis.
This paper aims to address the pancreas segmentation task in 3D computed
tomography volumes. We propose a novel end-to-end network, Globally Guided
Progressive Fusion Network, as an effective and efficient solution to
volumetric segmentation, which involves both global features and complicated 3D
geometric information. A progressive fusion network is devised to extract 3D
information from a moderate number of neighboring slices and predict a
probability map for the segmentation of each slice. An independent branch for
excavating global features from downsampled slices is further integrated into
the network. Extensive experimental results demonstrate that our method
achieves state-of-the-art performance on two pancreas datasets.
