@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 = {

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.

}, 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} } @article {1395, title = {Geometrical model-based segmentation of the organs of sight on CT images}, journal = {MEDICAL PHYSICS}, volume = {35}, year = {2008}, note = {UT: 000253318400036ScopusID: 38849194643doi: 10.1118/1.2826557}, month = {Feb 2008}, pages = {735 - 743}, type = {Journal article}, abstract = {

Segmentation of organs of sight such as the eyeballs, lenses,and optic nerves is a time consuming task for clinicians. The small size of the organs and the similar density of the surrounding tissues make the segmentation difficult. We developed a new algorithm to segment these organs with minimal user interaction. The algorithm needs only three seed points to fit an initial geometrical model to start an effective segmentation. The clinical evaluation shows that the output of our method is useful in clinical practice.

}, isbn = {0094-2405}, author = {Gy{\"o}rgy Bekes and E{\"o}rs M{\'a}t{\'e} and L{\'a}szl{\'o} G{\'a}bor Ny{\'u}l and Attila Kuba and M{\'a}rta Fidrich} } @article {1088, title = {3D segmentation of liver, kidneys and spleen from CT images}, journal = {INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY}, volume = {2}, year = {2007}, note = {ScopusID: 34250685687doi: 10.1007/s11548-007-0083-7}, month = {June 2007}, pages = {S45 - S47}, type = {Jounal article}, abstract = {

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.

}, isbn = {1861-6410}, author = {Gy{\"o}rgy Bekes and L{\'a}szl{\'o} G{\'a}bor Ny{\'u}l and E{\"o}rs M{\'a}t{\'e} and Attila Kuba and M{\'a}rta Fidrich} } @booklet {1185, title = {Computer Assisted Image Processing and Navigation System for Orthopedic-Trauma Surgery}, year = {2004}, note = {[El{\H o}ad{\'a}s] [El{\H o}ad{\'a}s] }, month = {2004///}, author = {Endre Varga and Kriszti{\'a}n Oll{\'e} and Bal{\'a}zs Erd{\H o}helyi and Gy{\"o}rgy Bekes and Krisztina Mar{\'o}ti and Attila Kuba} } @booklet {1178, title = {MedEdit. Orvosi K{\'e}pfeldolgoz{\'o} {\'e}s M{\H u}t{\'e}ti Tervez{\H o}/Elemz{\H o} Rendszer}, year = {2004}, note = {[El{\H o}ad{\'a}s] [El{\H o}ad{\'a}s] }, month = {2004///}, author = {Endre Varga and Bal{\'a}zs Erd{\H o}helyi and Kriszti{\'a}n Oll{\'e} and Krisztina Mar{\'o}ti and Gy{\"o}rgy Bekes and Attila Kuba} } @booklet {1187, title = {MedEdit{\'E} Orvosi k{\'e}pfeldolgoz{\'o}, tervez{\H o}/elemz{\H o} {\'e}s tervez{\H o} rendszer}, year = {2004}, note = {[El{\H o}ad{\'a}s] [El{\H o}ad{\'a}s] }, month = {2004///}, author = {Endre Varga and Kriszti{\'a}n Oll{\'e} and Bal{\'a}zs Erd{\H o}helyi and Krisztina Mar{\'o}ti and Gy{\"o}rgy Bekes and Attila Kuba} } @booklet {1188, title = {MedEdit{\'E} Orvosi tervez{\H o} {\'e}s okat{\'a}si rendszer}, year = {2004}, note = {[El{\H o}ad{\'a}s] [El{\H o}ad{\'a}s] }, month = {2004///}, author = {Endre Varga and Kriszti{\'a}n Oll{\'e} and Bal{\'a}zs Erd{\H o}helyi and Krisztina Mar{\'o}ti and Gy{\"o}rgy Bekes and Attila Kuba} }