<?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%">György Bekes</style></author><author><style face="normal" font="default" size="100%">László Gábor Nyúl</style></author><author><style face="normal" font="default" size="100%">Eörs Máté</style></author><author><style face="normal" font="default" size="100%">Attila Kuba</style></author><author><style face="normal" font="default" size="100%">Márta Fidrich</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">3D segmentation of liver, kidneys and spleen from CT images</style></title><secondary-title><style face="normal" font="default" size="100%">INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY</style></secondary-title><short-title><style face="normal" font="default" size="100%">INT J COMPUT ASSIST RADIOL SURG</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">June 2007</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">2</style></volume><pages><style face="normal" font="default" size="100%">S45 - S47</style></pages><isbn><style face="normal" font="default" size="100%">1861-6410</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The clinicians often need to segment the abdominal organs forradiotherapy planning. Manual segmentation of these organs is very time-consuming, therefore automated methods are desired. We developed a semi-automatic segmentation method to outline liver, spleen and kidneys. It works on CT images without contrast intake that are acquired with a routine clinical protocol. From an initial surface around a user defined seed point, the segmentation of the organ is obtained by an active surface algorithm. Pre- and post-processing steps are used to adapt the general method for specific organs. The evaluation results show that the accuracy of our method is about 90%, which can be further improved with little manual editing, and that the precision is slightly higher than that of manual contouring. Our method is accurate, precise and fast enough to use in the clinical practice.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1 SUPPL.</style></issue><work-type><style face="normal" font="default" size="100%">Jounal article</style></work-type><notes><style face="normal" font="default" size="100%">ScopusID: 34250685687doi: 10.1007/s11548-007-0083-7</style></notes></record></records></xml>