State-of-the-Art and Challenges in Pancreatic CT Segmentation: A Systematic Review of U-Net and Its Variants

Abstract

In medical image analysis, segmenting pancreatic CT images presents a significant challenge due to the complex anatomy of the pancreas and the generally low contrast of these images. Accurate pancreas segmentation is crucial in clinical scenarios, particularly for the diagnosis and treatment of pancreatic cancer. The U-Net architecture and its variations have achieved significant progress in deep learning-based image segmentation, especially in the context of pancreatic CT image segmentation. However, there is a noticeable gap in the comprehensive evaluation of their performance, limitations, and potential improvements specifically in this area. This systematic review aims to address this gap in the literature, focusing particularly on U-Net and its variants in pancreatic CT image segmentation. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this review includes relevant studies published since 2019 in the field of pancreatic segmentation. The findings illuminate the current limitations of these methods and establish a theoretical foundation for future research directions.

Keywords

• Pancreas segmentation
• U-Net
• CT imaging
• systematic review
• deep learning