U-Net and its variants for medical image segmentation: theory and applications

TL;DR

This paper reviews U-net, a highly effective medical image segmentation technique, highlighting its architecture, recent developments, and diverse applications across various imaging modalities.

Contribution

It provides a comprehensive overview of U-net's architectural innovations, recent trends, and its expanding use in medical imaging and beyond.

Findings

U-net is widely adopted across all major medical imaging modalities.

Recent innovations have enhanced U-net's segmentation accuracy and efficiency.

U-net has been successfully applied in diverse applications beyond segmentation.

Abstract

U-net is an image segmentation technique developed primarily for medical image analysis that can precisely segment images using a scarce amount of training data. These traits provide U-net with a very high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in all major image modalities from CT scans and MRI to X-rays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. As the potential of U-net is still increasing, in this review we look at the various developments that have been made in the U-net architecture and provide observations on recent trends. We examine the various innovations that have been made in deep learning and discuss how these tools facilitate U-net. Furthermore, we look at image modalities and application areas where U-net has been applied.