Automatic Pancreas Segmentation Using Double Adversarial Networks With Pyramidal Pooling Module
IEEE ACCESS(2021)
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.
MoreTranslated text
Key words
Image segmentation,Pancreas,Computed tomography,Generators,Three-dimensional displays,Task analysis,Biological systems,Generative adversarial network,pancreas segmentation,double adversarial networks,pyramidal pooling module
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined