%0 Journal Article %J IEEE TRANSACTIONS ON MEDICAL IMAGING %D 2009 %T Comparison and evaluation of methods for liver segmentation from CT datasets %A Tobias Heimann %A Brahm Van Ginneken %A Martin A Styner %A Yulia Arzhaeva %A Volker Aurich %A Christian Bauer %A Andreas Beck %A Christoph Becker %A Reinhardt Beichel %A György Bekes %A Fernando Bello %A Gerd Binnig %A Horst Bischof %A Alexander Bornik %A Peter MM Cashman %A Ying Chi %A Andres Córdova %A Benoit M Dawant %A Márta Fidrich %A Jacob D Furst %A Daisuke Furukawa %A Lars Grenacher %A Joachim Hornegger %A Dagmar Kainmüller %A Richard I Kitney %A Hidefumi Kobatake %A Hans Lamecker %A Thomas Lange %A Jeongjin Lee %A Brian Lennon %A Rui Li %A Senhu Li %A Hans-Peter Meinzer %A Gábor Németh %A Daniela S Raicu %A Anne-Mareike Rau %A Eva M Van Rikxoort %A Mikael Rousson %A László Ruskó %A Kinda A Saddi %A Günter Schmidt %A Dieter Seghers %A Akinobi Shimizu %A Pieter Slagmolen %A Erich Sorantin %A Grzegorz Soza %A Ruchaneewan Susomboon %A Jonathan M Waite %A Andreas Wimmer %A Ivo Wolf %X

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. © 2009 IEEE.

%B IEEE TRANSACTIONS ON MEDICAL IMAGING %C Price, K., Anything you can do, I can do better (no you can't) (1986) Comput. Vis. Graph. Image Process, 36 (2-3), pp. 387-391;S. G. Armato, G. McLennan, M. F. McNitt-Gray, C. R. Meyer, D. Yankelevitz, D. R. Aberle, C. I. Henschke, E. A. Hoffman, E. A. Ka %V 28 %P 1251 - 1265 %8 Aug 2009 %@ 0278-0062 %G eng %N 8 %9 Journal article %! IEEE T MED IMAGING %R 10.1109/TMI.2009.2013851 %0 Book Section %B Medical Imaging 2004: Image Processing %D 2004 %T Liver segment approximation in CT data for surgical resection planning %A Reinhardt Beichel %A Thomas Pock %A Christian Janko %A Roman B Zotter %A Bernhard Reitinger %A Alexander Bornik %A Kálmán Palágyi %A Erich Sorantin %A Georg Werkgartner %A Horst Bischof %A Milan Sonka %E J Michael Fitzpatrick %E Milan Sonka %X Surgical planning of liver tumor resections requires detailed three-dimensional (3D) understanding of the complex arrangement of vasculature, liver segments and tumors. Knowledge about location and sizes of liver segments is important for choosing an optimal surgical resection approach and predicting postoperative residual liver capacity. The aim of this work is to facilitate such surgical planning process by developing a robust method for portal vein tree segmentation. The work also investigates the impact of vessel segmentation on the approximation of liver segment volumes. For segment approximation, smaller portal vein branches are of importance. Small branches, however, are difficult to segment due to noise and partial volume effects. Our vessel segmentation is based on the original gray-values and on the result of a vessel enhancement filter. Validation of the developed portal vein segmentation method in computer generated phantoms shows that, compared to a conventional approach, more vessel branches can be segmented. Experiments with in vivo acquired liver CT data sets confirmed this result. The outcome of a Nearest Neighbor liver segment approximation method applied to phantom data demonstrates, that the proposed vessel segmentation approach translates into a more accurate segment partitioning. %B Medical Imaging 2004: Image Processing %I SPIE %C Bellingham; WashingtonScheele, J., Anatomical and atypical liver resection (2001) Chirurg, 72 (2), pp. 113-124;Couinaud, C., (1957) Le Foie - Etudes Anatomiques et Chirurgicales, , Masson, Paris; Strunk, H., Stuckmann, G., Textor, J., Willinek, W., Limit %P 1435 - 1446 %8 2004/// %G eng