Harnessing 2D Networks and 3D Features for Automated Pancreas Segmentation from Volumetric CT Images

Segmenting pancreas from abdominal CT scans is an important prerequisite for pancreatic cancer diagnosis and precise treatment planning. However, automated pancreas segmentation faces challenges posed by shape and size variances, low contrast with regard to adjacent tissues and in particular negligibly small proportion to the whole abdominal volume. Current coarse-to-fine frameworks, either using tri-planar schemes or stacking 2D pre-segmentation as prior to 3D networks, have limitation on effectively capturing 3D information. While iterative updates on region of interest (ROI) in refinement stage alleviate accumulated errors caused by coarse segmentation, extra computational burden is introduced. In this paper, we harness 2D networks and 3D features to improve segmentation accuracy and efficiency. Firstly, in the 3D coarse segmentation network, a new bias-dice loss function is defined to increase ROI recall rates to improve efficiency by avoiding iterative ROI refinements. Secondly, for a full utilization of 3D information, dimension adaptation module (DAM) is introduced to bridge 2D networks and 3D information. Finally, a fusion decision module and parallel training strategy are proposed to fuse multi-source feature cues extracted from three sub-networks to make final predictions. The proposed method is evaluated in NIH dataset and outperforms the state-of-the-art methods in comparison, with mean Dice-Sorensen coefficient (DSC) of 85.22%, and with averagely 0.4 min for each instance.