Abstract
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.
Highlights
Automatic segmentation of the pancreas from abdominal CT images is a challenging task owing to the limited volume and variable shape of the pancreas in abdominal scans
We propose an automatic segmentation model using double adversarial networks and a pyramidal pooling module to segment the pancreas from abdominal CT images
The second involvement of adversarial learning further accelerates the preservation of spatial information and causes the output segmentation results to be more realistic with respect to the standard volumes, i.e., the involved double adversarial networks repeat the above-mentioned process to further boost the segmentation network performance
Summary
Automatic segmentation of the pancreas from abdominal CT images is a challenging task owing to the limited volume and variable shape of the pancreas in abdominal scans. Compared with other bulky abdominal organs, the limited volume of the pancreas makes it difficult to segment it from CT images using only simple deep -learning-based methods. A generative adversarial network consists of two competing networks, i.e., a discriminator and a generator, where the generator attempts to produce samples to deceive the discriminator while the discriminator aims to distinguish the synthetic samples from the real images regardless of how similar they are This special competing mechanism helps capture highdimensional dataset distributions, preserving more useful information for segmentation. We propose an automatic segmentation model using double adversarial networks and a pyramidal pooling module to segment the pancreas from abdominal CT images. The second involvement of adversarial learning further accelerates the preservation of spatial information and causes the output segmentation results to be more realistic with respect to the standard volumes, i.e., the involved double adversarial networks repeat the above-mentioned process to further boost the segmentation network performance. (2) In addition, we include a novel pyramidal pooling module in the proposed architecture to help capture
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