Automatic Pancreas Segmentation Using Double Adversarial Networks With Pyramidal Pooling Module

Owing to the irregular shape and high anatomical variability of the pancreas in abdominal CT images, pancreas segmentation is regarded as a challenging task. To address this issue, we propose an automatic segmentation model using double adversarial networks with a pyramidal pooling module. First, we introduce double adversarial networks that double-check whether the obtained segmentation results are similar to their ground truths owing to the special competing mechanism of adversarial learning, which contributes to the capturing of spatial information for segmentation and prompts the obtained samples to be more realistic, to improve the network segmentation performance. Second, we design a pyramidal pooling module to collect multi-level features and retain substantial information for segmentation in order to further boost the network performance. Finally, to assess the segmentation performance of our model, we use several indexes, namely the Dice similarity coefficient (DSC), Jaccard index, precision, and recall, as evaluation indicators. Experimental results show that the proposed model outperforms most existing pancreas segmentation methods.