mlsanityData to 14 Oct 2026

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

arXiv 2101.00232 ↗First posted 1 Jan 2021eess.IV cs.CVcited by 7

Not on the map. Why?

Abstract (arXiv)

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 urlhttps://github.com/JunMa11/SOTA-MedSeg.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 42 of its 64 references that are in the map. First posted on arXiv 1 Jan 2021.

Builds on

42 of 64 references.

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30 more
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Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 3; 2023: 0; 2024: 2; 2025: 1.

7 papers.

  1. 2021The Medical Segmentation Decathloncited by 628
  2. 2024U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentationcited by 419
  3. 2022Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentationcited by 76
  4. 2022Extending nnU-Net is all you needcited by 22
  5. 2025Federated nnU-Net for Privacy-Preserving Medical Image Segmentationcited by 3
  6. 2024Quantifying the Impact of Population Shift Across Age and Sex for Abdominal Organ Segmentationcited by 2
  7. 2022Calibrating Ensembles for Scalable Uncertainty Quantification in Deep Learning-based Medical Segmentationcited by 0