Abstract

Recently 3D volumetric organ segmentation attracts much research interest in medical image analysis due to its significance in com- puter aided diagnosis. This paper aims to address the pancreas segmen- tation 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 pro- gressive fusion network is devised to extract 3D information from a mod- erate 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.