Log In Sign Up

Cutting-edge 3D Medical Image Segmentation Methods in 2020: Are Happy Families All Alike?

by   Jun Ma, et al.

Segmentation is one of the most important and popular tasks in medical image analysis, which plays a critical role in disease diagnosis, surgical planning, and prognosis evaluation. During the past five years, on the one hand, thousands of medical image segmentation methods have been proposed for various organs and lesions in different medical images, which become more and more challenging to fairly compare different methods. On the other hand, international segmentation challenges can provide a transparent platform to fairly evaluate and compare different methods. In this paper, we present a comprehensive review of the top methods in ten 3D medical image segmentation challenges during 2020, covering a variety of tasks and datasets. We also identify the "happy-families" practices in the cutting-edge segmentation methods, which are useful for developing powerful segmentation approaches. Finally, we discuss open research problems that should be addressed in the future. We also maintain a list of cutting-edge segmentation methods at <>.


page 1

page 4

page 5

page 6

page 7

page 8

page 9


U-Net and its variants for Medical Image Segmentation : A short review

The paper is a short review of medical image segmentation using U-Net an...

Panoptic Segmentation: A Review

Image segmentation for video analysis plays an essential role in differe...

A survey on shape-constraint deep learning for medical image segmentation

Since the advent of U-Net, fully convolutional deep neural networks and ...

Improving Uncertainty-based Out-of-Distribution Detection for Medical Image Segmentation

Deep Learning models are easily disturbed by variations in the input ima...

Deep Learning for Medical Image Segmentation: Tricks, Challenges and Future Directions

Over the past few years, the rapid development of deep learning technolo...

SenseCare: A Research Platform for Medical Image Informatics and Interactive 3D Visualization

Clinical research on smart healthcare has an increasing demand for intel...

Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models

Despite the remarkable performance of deep learning methods on various t...