Machine Learning-based Segmentation

Machine Learning-Based Segmentation is a computational method that uses machine learning algorithms to automatically partition patient or volume data into distinct, meaningful subgroups or regions based on learned patterns from training data, distinguishing it from traditional rule-based or manual segmentation techniques.

320

Publications

18.7K

Citations

1.5K

Authors

668

Institutions

Publications per year

2017–2026

227

Authors

1.5K

  1. YZ
    Yefeng Zheng

    Tencent (China), China

    4 publications · 146 citations

    4

    h-index

  2. JW
    Jürgen Weitz

    Heidelberg University, Germany

    3 publications · 94 citations

    3

    h-index

  3. SL
    Stefan Leger

    University Hospital Carl Gustav Carus, Germany

    3 publications · 94 citations

    3

    h-index

  4. SB
    Sebastian Bodenstedt

    Heidelberg University, Germany

    3 publications · 94 citations

    3

    h-index

  5. SS
    Stefanie Speidel

    Heidelberg University, Germany

    3 publications · 94 citations

    3

    h-index

1–5 of 1.5K

Venues

  1. IEEE Transactions on Medical Imaging

    8 publications · 1.9K citations

    8

    h-index

  2. Medical Image Analysis

    6 publications · 508 citations

    7

    h-index

  3. Physics in Medicine and Biology

    6 publications · 314 citations

    6

    h-index

  4. Scientific Reports

    5 publications · 165 citations

    6

    h-index

  5. arXiv (Cornell University)

    Repository · 5 publications · 265 citations

    5

    h-index

1–5