Liver segmentation from computed tomography scans: A survey and a new algorithm

Paola Campadelli, Elena Casiraghi, Andrea Esposito

Research output: Contribution to journalArticlepeer-review


Objective: In the recent years liver segmentation from computed tomography scans has gained a lot of importance in the field of medical image processing since it is the first and fundamental step of any automated technique for the automatic liver disease diagnosis, liver volume measurement, and 3D liver volume rendering. Methods: In this paper we report a review study about the semi-automatic and automatic liver segmentation techniques, and we describe our fully automatized method. Results: The survey reveals that automatic liver segmentation is still an open problem since various weaknesses and drawbacks of the proposed works must still be addressed. Our gray-level based liver segmentation method has been developed to tackle all these problems; when tested on 40 patients it achieves satisfactory results, comparable to the mean intra- and inter-observer variation. Conclusions: We believe that our technique outperforms those presented in the literature; nevertheless, a common test set with its gold standard traced by experts, and a generally accepted performance measure are required to demonstrate it.

Original languageEnglish
Pages (from-to)185-196
Number of pages12
JournalArtificial Intelligence in Medicine
Issue number2-3
Publication statusPublished - Feb 2009


  • Automatic liver segmentation
  • Computed tomography images
  • Graph cut
  • Survey

ASJC Scopus subject areas

  • Artificial Intelligence
  • Medicine (miscellaneous)


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