Segmentation Quality Metrics

Segmentation Quality Metrics is a formal evaluation framework used in image analysis and computer vision that quantifies the accuracy and consistency of image segmentation algorithms by comparing their output against ground truth labels through specific mathematical measures such as intersection-over-union, Dice coefficient, or boundary fidelity.

416

Publications

41.5K

Citations

1.7K

Authors

685

Institutions

Publications per year

2017–2026

170

Authors

1.7K

  1. JK
    Jayaram K. Udupa

    University of Pennsylvania, United States

    8 publications · 704 citations

    8

    h-index

  2. KW
    Kevin W. Bowyer

    University of Notre Dame, United States

    5 publications · 879 citations

    5

    h-index

  3. XJ
    Xiaoyi Jiang

    University of Münster, Germany

    5 publications · 987 citations

    5

    h-index

  4. AA
    Abdel Aziz Taha

    Research Studios Austria, Austria

    5 publications · 3K citations

    5

    h-index

  5. ED
    Elisa Drelie Gelasca

    École Polytechnique Fédérale de Lausanne, Switzerland

    5 publications · 254 citations

    5

    h-index

1–5 of 1.7K

Venues

  1. arXiv (Cornell University)

    Repository · 17 publications · 464 citations

    14

    h-index

  2. 10

    h-index

  3. Medical Image Analysis

    9 publications · 488 citations

    9

    h-index

  4. IEEE Transactions on Image Processing

    9 publications · 533 citations

    9

    h-index

  5. IEEE Access

    9 publications · 374 citations

    9

    h-index

1–5