Comparison and evaluation of methods for liver segmentation from CT datasets

Christian Bauer, Horst Bischof, Reinhard Beichel, Alexander Bornik, Tobias Heimann, Bram van Ginneken, Martin Styner, Yulia Arzhaeva, Volker Aurich, Andreas Beck, Christoph Becker, György Bekes, Fernando Bello, Gerd Binnig, Peter M. M. Cashman, Ying Chi, Andrés Cordova, Benoit M. Dawant, Márta Fidrich Fidrich, Jacob D. FurstDaisuke Furukawa, Lars Grenacher, Hornegger Hornegger, Dagmar Kainmüller, Richard I. Kitney, Hidefumi Kobatake, Hans Lamecker, Thomas Lange, Jeongjin Lee, Brian Lennon, Rui Li, Senhu Li, Hans-Peter Meinzer, Gábor Nemeth, Daniela S. Raicu, Anne-Mareike Rau, Eva M. van Rikxoort, Mikael Rousson, László Rusko, Kinda A. Saddi, Günter Schmidt, Dieter Seghers, Akinobu Shimizu, Pieter Slagmolen, Erich Sorantin, Grzegorz Soza, Ruchaneewan Susomboon, Jonathan M. Waite, Andreas Wimmer, Ivo Wolf

Research output: Contribution to journalArticlepeer-review


This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results from the “MICCAI 2007 Grand Challenge” workshop, where 16 teams evaluated their algorithms on a common database. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Participants were also allowed to use additional proprietary training data for that purpose. All teams then had to apply their methods to 10 test datasets and submit the obtained results. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to reference segmentations five error measures that highlight different aspects of segmentation accuracy. All measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.
Original languageEnglish
Pages (from-to)1251-1265
JournalIEEE Transactions on Medical Imaging
Issue number8
Publication statusPublished - 2009


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