Pancreatic cancer detection through semantic segmentation of CT images: a short review

Abstract

Detection of cancer in human organs at an early stage is a crucial task and is important for the survival of the patients, especially in terms of complex structure, dynamic size, and dynamic length in organs like the pancreas. To deal with this problem, pancreatic semantic segmentation was introduced, but it was hampered by challenges related to image modalities and the availability of limited datasets. This paper provides different deep learning models for pancreatic detection. The proposed model pipeline has two phases: pancreas localization and segmentation. In the first phase, rough regions of the pancreas are detected with YOLOv5, and the detected regions are cropped to avoid an imbalance between the pancreas region and the background. In the second phase, the detected regions are segmented with various models like UNet, VNet, SegResNet and HighResNet for effective detection of cancer regions. The experiments were conducted on a private dataset collected from the Champalimaud Foundation in Portugal. The model's performance is evaluated in terms of quantitative and qualitative analysis. From experiments, we found that, when compared to other Nets, YOLOv5 is superior in pancreatic area localization and 2.5D HighResNet is superior in segmentation.