Robust-Seed: seed-based segmentation improvement by optimisation

Achia Kronman, Leo Joskowicz*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Many medical image segmentation methods require the selection of seed points inside the target structure. Often times, the location of these seed points determines the accuracy of the resulting target structure delineation and may lead to undesirably high delineation variability. We present Robust-Seed, a new method for automatically reducing the variability of manual and semi-automatic seed-based segmentation methods with respect to the seed point location without compromising the target structure segmentation accuracy. The inputs are a volumetric image, a seed point inside the target structure, and a seed-based segmentation method. The output is a new seed point that optimises the target structure segmentation result. The algorithm iteratively computes a new seed point location that improves the expected target structure segmentation for the given method. Experimental evaluation of seed-based fast-marching level-set and adaptive region growing segmentation of the kidney and the liver on 32 CT scans with ground-truth delineations shows that Robust-Seed yields a perfect robustness score with no significant compromise on the segmentation quality (paired t-test, p < 0.05). The key advantages of Robust-Seed are that it is automatic, that it is independent of target structure and segmentation method used, and that it applies to a wide class of anatomical structures and clinical tasks.

Original languageAmerican English
Pages (from-to)564-572
Number of pages9
JournalComputer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization
Issue number5
StatePublished - 3 Sep 2018

Bibliographical note

Publisher Copyright:
© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.


  • Medical image segmentation
  • segmentation optimisation
  • segmentation robustness


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