Title: Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets
Authors: Heimann, Tobias ×
van Ginneken, Bram
Styner, Martin A.
Arzhaeva, Yulia
Aurich, Volker
Bauer, Christian
Beck, Andreas
Becker, Christoph
Beichel, Reinhard
Bekes, György
Bello, Fernando
Binnig, Gerd
Bischof, Horst
Bornik, Alexander
Cashman, Peter M.M.
Chi, Ying
Córdova, Andrés
Dawant, Benoit M.
Fidrich, Márta
Furst, Jacob D.
Furukawa, Daisuke
Grenacher, Lars
Hornegger, Joachim
Kainmüller, Dagmar
Kitney, Richard I.
Kobatake, Hidefumi
Lamecker, Hans
Lange, Thomas
Lee, Jeongjin
Lennon, Brian
Meinzer, Hans-Peter
Németh, Gábor
Raicu, Daniela S.
Rau, Anne-Mareike
van Rikxoort, Eva M.
Rousson, Mikaël
Ruskó, László
Saddi, Kinda A.
Schmidt, Günter
Seghers, Dieter
Shimizu, Akinobu
Slagmolen, Pieter
Sorantin, Erich
Soza, Grzegorz
Susomboon, Ruchaneewan
Waite, Jonathan M.
Wimmer, Andreas
Wolf, Ivo
Li, Rui
Li, Senhu #
Issue Date: Aug-2009
Publisher: Institute of Electrical and Electronics Engineers
Series Title: IEEE Transactions on Medical Imaging vol:28 issue:8 pages:1251-1265
Abstract: 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.
Description: Heimann T., van Ginneken B., Styner M.A., Arzhaeva Y., Aurich V., Bauer C., Beck A., Becker C., Beichel R., Bekes G., Bello F., Binnig G., Bischof H., Bornik A., Cashman P.M.M., Chi Y., Córdova A., Dawant B.M., Fidrich M., Furst J.D., Furukawa D., Grenacher L., Hornegger J., Kainmüller D., Kitney R.I., Kobatake H., Lamecker H., Lange T., Lee J., Lennon B., Li R., Li S., Meinzer H.-P., Németh G., Raicu D.S., Rau A.-M., van Rikxoort E.M., Rousson M., Ruskó L., Saddi K.A., Schmidt G., Seghers D., Shimizu A., Slagmolen P., Sorantin E., Soza G., Susomboon R., Waite J.M., Wimmer A., Wolf I., ''Comparison and evaluation of methods for liver segmentation from CT datasets'', IEEE transactions on medical imaging, vol. 28, no. 8, pp. 1251-1265, August 2009.
ISSN: 0278-0062
Publication status: published
KU Leuven publication type: IT
Appears in Collections:ESAT - PSI, Processing Speech and Images
Laboratory of Experimental Radiotherapy
× corresponding author
# (joint) last author

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