Automatic Pancreatic Cyst Lesion Segmentation on EUS Images Using a Deep-Learning Approach

The automatic segmentation of the pancreatic cyst lesion (PCL) is essential for the automated diagnosis of pancreatic cyst lesions on endoscopic ultrasonography (EUS) images. In this study, we proposed a deep-learning approach for PCL segmentation on EUS images. We employed the Attention U-Net model for automatic PCL segmentation. The Attention U-Net was compared with the Basic U-Net, Residual U-Net, and U-Net++ models. The Attention U-Net showed a better dice similarity coefficient (DSC) and intersection over union (IoU) scores than the other models on the internal test. Although the Basic U-Net showed a higher DSC and IoU scores on the external test than the Attention U-Net, there was no statistically significant difference. On the internal test of the cross-over study, the Attention U-Net showed the highest DSC and IoU scores. However, there was no significant difference between the Attention U-Net and Residual U-Net or between the Attention U-Net and U-Net++. On the external test of the cross-over study, all models showed no significant difference from each other. To the best of our knowledge, this is the first study implementing segmentation of PCL on EUS images using a deep-learning approach. Our experimental results show that a deep-learning approach can be applied successfully for PCL segmentation on EUS images.

Automated summary

The automatic segmentation of pancreatic cyst lesions on endoscopic ultrasonography (EUS) images was achieved using a deep-learning approach. The Attention U-Net model showed superior performance in dice similarity coefficient (DSC) and intersection over union (IoU) scores compared to other models in the internal test. However, there was no statistically significant difference between the Attention U-Net and the Basic U-Net model in the external test.