@article {48171, title = {Comparison and evaluation of methods for liver segmentation from CT datasets.}, journal = {IEEE transactions on medical imaging}, volume = {28}, year = {2009}, month = {2009 Aug}, pages = {1251-65}, 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.

}, keywords = {Algorithms, Bayes Theorem, Databases, Factual, Humans, Image Processing, Computer-Assisted, Liver, Tomography, X-Ray Computed}, issn = {1558-254X}, doi = {10.1109/TMI.2009.2013851}, author = {Heimann, Tobias and van Ginneken, Bram and Styner, Martin A and Arzhaeva, Yulia and Aurich, Volker and Bauer, Christian and Beck, Andreas and Becker, Christoph and Beichel, Reinhard and Bekes, Gy{\"o}rgy and Bello, Fernando and Binnig, Gerd and Bischof, Horst and Bornik, Alexander and Cashman, Peter M M and Chi, Ying and Cordova, Andr{\'e}s and Dawant, Benoit M and Fidrich, M{\'a}rta and Furst, Jacob D and Furukawa, Daisuke and Grenacher, Lars and Hornegger, Joachim and Kainm{\"u}ller, Dagmar and Kitney, Richard I and Kobatake, Hidefumi and Lamecker, Hans and Lange, Thomas and Lee, Jeongjin and Lennon, Brian and Li, Rui and Li, Senhu and Meinzer, Hans-Peter and Nemeth, G{\'a}bor and Raicu, Daniela S and Rau, Anne-Mareike and van Rikxoort, Eva M and Rousson, Mika{\"e}l and Rusko, L{\'a}szl{\'o} and Saddi, Kinda A and Schmidt, G{\"u}nter and Seghers, Dieter and Shimizu, Akinobu and Slagmolen, Pieter and Sorantin, Erich and Soza, Grzegorz and Susomboon, Ruchaneewan and Waite, Jonathan M and Wimmer, Andreas and Wolf, Ivo} } @conference {48185, title = {Shape constrained automatic segmentation of the liver based on a heuristic intensity model.}, booktitle = {MICCAI Workshop 3D Segmentation in the Clinic}, year = {2007}, month = {2007 Oct 29}, address = {Brisbane, Australia}, abstract = {

We present a fully automatic 3D segmentation method for the liver from contrast-enhanced CT data. It is based on a combination of a constrained free-form and statistical deformable model. The adap- tation of the model to the image data is performed according to a simple model of the typical intensity distribution around the liver boundary and neighboring anatomical structures, considering the potential presence of tumors in the liver. All parameters of the deformation as well as the initial positioning of the model in the data are estimated automatically.\ 

}, url = {http://mbi.dkfz-heidelberg.de/grand-challenge2007/web/p109.pdf}, author = {Kainmueller, Dagmar and Lange, Thomas and Lamecker, Hans} }