Main Menu (Mobile)- Block

Main Menu - Block

Comparison and evaluation of methods for liver segmentation from CT datasets.

Our Research / Publications
janelia7_blocks-janelia7_biblio_header | block
IEEE transactions on medical imaging. 2009 Aug;28(8):1251-65. doi: 10.1109/TMI.2009.2013851
View Article on
Comparison and evaluation of methods for liver segmentation from CT datasets. Kainmueller Lab
Heimann T, van Ginneken B, Styner MA, Arzhaeva Y, Aurich V, Bauer C, Beck A, Becker C, Beichel R, Bekes G, Bello F, Binnig G, Bischof H, Bornik A, Cashman PM, Chi Y, Cordova A, Dawant BM, Fidrich M, Furst JD, Furukawa D, Grenacher L, Hornegger J, Kainmüller D, Kitney RI, Kobatake H, Lamecker H, Lange T, Lee J, Lennon B, Li R, Li S, Meinzer H, Nemeth G, Raicu DS, Rau A, van Rikxoort EM, Rousson M, Rusko L, Saddi KA, Schmidt G, Seghers D, Shimizu A, Slagmolen P, Sorantin E, Soza G, Susomboon R, Waite JM, Wimmer A, Wolf I
Note: Research in this publication was not performed at Janelia.
janelia7_blocks-janelia7_biblio_abstract | block

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.

PMID: 19211338 [PubMed - indexed for MEDLINE]

node:body | entity_field
janelia7_blocks-janelia7_biblio_authors | block

Janelia Authors

janelia7_blocks-janelia7_biblio_tools | block