  Due to the fact that pancreas is an abdominal organ with very large
variations in shape and size, automatic and accurate pancreas segmentation can
be challenging for medical image analysis. In this work, we proposed a fully
automated two stage framework for pancreas segmentation based on convolutional
neural networks (CNN). In the first stage, a U-Net is trained for the
down-sampled 3D volume segmentation. Then a candidate region covering the
pancreas is extracted from the estimated labels. Motivated by the superior
performance reported by renowned region based CNN, in the second stage, another
3D U-Net is trained on the candidate region generated in the first stage. We
evaluated the performance of the proposed method on the NIH computed tomography
(CT) dataset, and verified its superiority over other state-of-the-art 2D and
3D approaches for pancreas segmentation in terms of dice-sorensen coefficient
(DSC) accuracy in testing. The mean DSC of the proposed method is 85.99%.
