  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
\url{https://github.com/JunMa11/SOTA-MedSeg}.
