Automated abdominal multi-organ segmentation with subject-specific atlas generation

Robin Wolz, Chengwen Chu, Kazunari Misawa, Michitaka Fujiwara, Kensaku Mori, Daniel Rueckert

Research output: Contribution to journalArticlepeer-review

239 Scopus citations


A robust automated segmentation of abdominal organs can be crucial for computer aided diagnosis and laparoscopic surgery assistance. Many existing methods are specialized to the segmentation of individual organs and struggle to deal with the variability of the shape and position of abdominal organs. We present a general, fully-automated method for multi-organ segmentation of abdominal computed tomography (CT) scans. The method is based on a hierarchical atlas registration and weighting scheme that generates target specific priors from an atlas database by combining aspects from multi-atlas registration and patch-based segmentation, two widely used methods in brain segmentation. The final segmentation is obtained by applying an automatically learned intensity model in a graph-cuts optimization step, incorporating high-level spatial knowledge. The proposed approach allows to deal with high inter-subject variation while being flexible enough to be applied to different organs. We have evaluated the segmentation on a database of 150 manually segmented CT images. The achieved results compare well to state-of-the-art methods, that are usually tailored to more specific questions, with Dice overlap values of 94%, 93%, 70%, and 92% for liver, kidneys, pancreas, and spleen, respectively.

Original languageEnglish
Article number6522848
Pages (from-to)1723-1730
Number of pages8
JournalIEEE Transactions on Medical Imaging
Issue number9
StatePublished - 2013
Externally publishedYes


  • Abdominal computed tomography (CT)
  • graph cuts
  • hierarchical model
  • multi-atlas segmentation
  • patch-based segmentation


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