Automatic Pancreatic Cyst Lesion Segmentation on EUS Images Using a Deep-Learning Approach
- Authors
- Oh, Seok; Kim, Young-Jae; Park, Young-Taek; Kim, Kwang-Gi
- Issue Date
- Jan-2022
- Publisher
- MDPI
- Keywords
- Computer-aided diagnosis; Deep learning; Endoscopic ultrasonography; Pancreatic cyst lesion; Segmentation
- Citation
- Sensors, v.22, no.1
- Journal Title
- Sensors
- Volume
- 22
- Number
- 1
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/83353
- DOI
- 10.3390/s22010245
- ISSN
- 1424-8220
- Abstract
- 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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
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