<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tobias Heimann</style></author><author><style face="normal" font="default" size="100%">Brahm Van Ginneken</style></author><author><style face="normal" font="default" size="100%">Martin A Styner</style></author><author><style face="normal" font="default" size="100%">Yulia Arzhaeva</style></author><author><style face="normal" font="default" size="100%">Volker Aurich</style></author><author><style face="normal" font="default" size="100%">Christian Bauer</style></author><author><style face="normal" font="default" size="100%">Andreas Beck</style></author><author><style face="normal" font="default" size="100%">Christoph Becker</style></author><author><style face="normal" font="default" size="100%">Reinhardt Beichel</style></author><author><style face="normal" font="default" size="100%">György Bekes</style></author><author><style face="normal" font="default" size="100%">Fernando Bello</style></author><author><style face="normal" font="default" size="100%">Gerd Binnig</style></author><author><style face="normal" font="default" size="100%">Horst Bischof</style></author><author><style face="normal" font="default" size="100%">Alexander Bornik</style></author><author><style face="normal" font="default" size="100%">Peter MM Cashman</style></author><author><style face="normal" font="default" size="100%">Ying Chi</style></author><author><style face="normal" font="default" size="100%">Andres Córdova</style></author><author><style face="normal" font="default" size="100%">Benoit M Dawant</style></author><author><style face="normal" font="default" size="100%">Márta Fidrich</style></author><author><style face="normal" font="default" size="100%">Jacob D Furst</style></author><author><style face="normal" font="default" size="100%">Daisuke Furukawa</style></author><author><style face="normal" font="default" size="100%">Lars Grenacher</style></author><author><style face="normal" font="default" size="100%">Joachim Hornegger</style></author><author><style face="normal" font="default" size="100%">Dagmar Kainmüller</style></author><author><style face="normal" font="default" size="100%">Richard I Kitney</style></author><author><style face="normal" font="default" size="100%">Hidefumi Kobatake</style></author><author><style face="normal" font="default" size="100%">Hans Lamecker</style></author><author><style face="normal" font="default" size="100%">Thomas Lange</style></author><author><style face="normal" font="default" size="100%">Jeongjin Lee</style></author><author><style face="normal" font="default" size="100%">Brian Lennon</style></author><author><style face="normal" font="default" size="100%">Rui Li</style></author><author><style face="normal" font="default" size="100%">Senhu Li</style></author><author><style face="normal" font="default" size="100%">Hans-Peter Meinzer</style></author><author><style face="normal" font="default" size="100%">Gábor Németh</style></author><author><style face="normal" font="default" size="100%">Daniela S Raicu</style></author><author><style face="normal" font="default" size="100%">Anne-Mareike Rau</style></author><author><style face="normal" font="default" size="100%">Eva M Van Rikxoort</style></author><author><style face="normal" font="default" size="100%">Mikael Rousson</style></author><author><style face="normal" font="default" size="100%">László Ruskó</style></author><author><style face="normal" font="default" size="100%">Kinda A Saddi</style></author><author><style face="normal" font="default" size="100%">Günter Schmidt</style></author><author><style face="normal" font="default" size="100%">Dieter Seghers</style></author><author><style face="normal" font="default" size="100%">Akinobi Shimizu</style></author><author><style face="normal" font="default" size="100%">Pieter Slagmolen</style></author><author><style face="normal" font="default" size="100%">Erich Sorantin</style></author><author><style face="normal" font="default" size="100%">Grzegorz Soza</style></author><author><style face="normal" font="default" size="100%">Ruchaneewan Susomboon</style></author><author><style face="normal" font="default" size="100%">Jonathan M Waite</style></author><author><style face="normal" font="default" size="100%">Andreas Wimmer</style></author><author><style face="normal" font="default" size="100%">Ivo Wolf</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Comparison and evaluation of methods for liver segmentation from CT datasets</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE TRANSACTIONS ON MEDICAL IMAGING</style></secondary-title><short-title><style face="normal" font="default" size="100%">IEEE T MED IMAGING</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Aug 2009</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">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</style></pub-location><volume><style face="normal" font="default" size="100%">28</style></volume><pages><style face="normal" font="default" size="100%">1251 - 1265</style></pages><isbn><style face="normal" font="default" size="100%">0278-0062</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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 &quot;MICCAI 2007 Grand Challenge&quot; 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.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">8</style></issue><work-type><style face="normal" font="default" size="100%">Journal article</style></work-type><notes><style face="normal" font="default" size="100%">ScopusID: 68249121543doi: 10.1109/TMI.2009.2013851</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Reinhardt Beichel</style></author><author><style face="normal" font="default" size="100%">Thomas Pock</style></author><author><style face="normal" font="default" size="100%">Christian Janko</style></author><author><style face="normal" font="default" size="100%">Roman B Zotter</style></author><author><style face="normal" font="default" size="100%">Bernhard Reitinger</style></author><author><style face="normal" font="default" size="100%">Alexander Bornik</style></author><author><style face="normal" font="default" size="100%">Kálmán Palágyi</style></author><author><style face="normal" font="default" size="100%">Erich Sorantin</style></author><author><style face="normal" font="default" size="100%">Georg Werkgartner</style></author><author><style face="normal" font="default" size="100%">Horst Bischof</style></author><author><style face="normal" font="default" size="100%">Milan Sonka</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">J Michael Fitzpatrick</style></author><author><style face="normal" font="default" size="100%">Milan Sonka</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Liver segment approximation in CT data for surgical resection planning</style></title><secondary-title><style face="normal" font="default" size="100%">Medical Imaging 2004: Image Processing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2004///</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">SPIE</style></publisher><pub-location><style face="normal" font="default" size="100%">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</style></pub-location><pages><style face="normal" font="default" size="100%">1435 - 1446</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract><notes><style face="normal" font="default" size="100%">ScopusID: 5644267870doi: 10.1117/12.535514</style></notes></record></records></xml>