@article {873,
	title = {Comparison and evaluation of methods for liver segmentation from CT datasets},
	journal = {IEEE TRANSACTIONS ON MEDICAL IMAGING},
	volume = {28},
	year = {2009},
	note = {ScopusID: 68249121543doi: 10.1109/TMI.2009.2013851},
	month = {Aug 2009},
	pages = {1251 - 1265},
	type = {Journal article},
	address = {Price, K., Anything you can do, I can do better (no you can{\textquoteright}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},
	abstract = {<p>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. {\textcopyright} 2009 IEEE.</p>},
	isbn = {0278-0062},
	doi = {10.1109/TMI.2009.2013851},
	author = {Tobias Heimann and Brahm Van Ginneken and Martin A Styner and Yulia Arzhaeva and Volker Aurich and Christian Bauer and Andreas Beck and Christoph Becker and Reinhardt Beichel and Gy{\"o}rgy Bekes and Fernando Bello and Gerd Binnig and Horst Bischof and Alexander Bornik and Peter MM Cashman and Ying Chi and Andres C{\'o}rdova and Benoit M Dawant and M{\'a}rta Fidrich and Jacob D Furst and Daisuke Furukawa and Lars Grenacher and Joachim Hornegger and Dagmar Kainm{\"u}ller and Richard I Kitney and Hidefumi Kobatake and Hans Lamecker and Thomas Lange and Jeongjin Lee and Brian Lennon and Rui Li and Senhu Li and Hans-Peter Meinzer and G{\'a}bor N{\'e}meth and Daniela S Raicu and Anne-Mareike Rau and Eva M Van Rikxoort and Mikael Rousson and L{\'a}szl{\'o} Rusk{\'o} and Kinda A Saddi and G{\"u}nter Schmidt and Dieter Seghers and Akinobi Shimizu and Pieter Slagmolen and Erich Sorantin and Grzegorz Soza and Ruchaneewan Susomboon and Jonathan M Waite and Andreas Wimmer and Ivo Wolf}
}
@inbook {930,
	title = {Liver segment approximation in CT data for surgical resection planning},
	booktitle = {Medical Imaging 2004: Image Processing},
	year = {2004},
	note = {ScopusID: 5644267870doi: 10.1117/12.535514},
	month = {2004///},
	pages = {1435 - 1446},
	publisher = {SPIE},
	organization = {SPIE},
	address = {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},
	abstract = {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.}
}
