00731nas a2200205 4500008003900000020002200039245005600061210005600117260008600173300001000259100001900269700002300288700002300311700002000334700001800354700002100372700001900393700002000412856009300432 2015 d a978-615-5036-10-100aÚjszülöttek monitorozása képfolyam elemzéssel0 aÚjszülöttek monitorozása képfolyam elemzéssel aVeszprém, HungarybNeumann János Számítógép-tudományi TársaságcNov 2015 a32-371 aNemeth, Jozsef1 aBánhalmi, András1 aNyúl, László, G1 aFidrich, Márta1 aSzkiva, Zsolt1 aFranczia, Péter1 aBerezki, Csaba1 aBilicki, Vilmos uhttps://www.inf.u-szeged.hu/en/publication/ujszulottek-monitorozasa-kepfolyam-elemzessel03873nas a2200733 4500008004100000020001400041245008100055210006900136260027000205300001600475490000700491520149600498100002001994700002402014700002202038700002002060700001902080700002102099700001802120700002202138700002302160700001902183700002002202700001702222700001902239700002202258700002302280700001402303700002102317700002202338700002002360700002002380700002202400700002002422700002302442700002402465700002302489700002302512700001902535700001802554700001802572700001802590700001202608700001402620700002402634700002002658700002202678700002202700700002502722700002002747700002102767700002002788700002102808700002002829700002102849700002202870700002002892700001902912700002702931700002302958700002002981700001403001856012403015 2009 eng d a0278-006200aComparison and evaluation of methods for liver segmentation from CT datasets0 aComparison and evaluation of methods for liver segmentation from aPrice, 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. KacAug 2009 a1251 - 12650 v283 a
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.
1 aHeimann, Tobias1 aVan Ginneken, Brahm1 aStyner, Martin, A1 aArzhaeva, Yulia1 aAurich, Volker1 aBauer, Christian1 aBeck, Andreas1 aBecker, Christoph1 aBeichel, Reinhardt1 aBekes, György1 aBello, Fernando1 aBinnig, Gerd1 aBischof, Horst1 aBornik, Alexander1 aCashman, Peter, MM1 aChi, Ying1 aCórdova, Andres1 aDawant, Benoit, M1 aFidrich, Márta1 aFurst, Jacob, D1 aFurukawa, Daisuke1 aGrenacher, Lars1 aHornegger, Joachim1 aKainmüller, Dagmar1 aKitney, Richard, I1 aKobatake, Hidefumi1 aLamecker, Hans1 aLange, Thomas1 aLee, Jeongjin1 aLennon, Brian1 aLi, Rui1 aLi, Senhu1 aMeinzer, Hans-Peter1 aNémeth, Gábor1 aRaicu, Daniela, S1 aRau, Anne-Mareike1 aVan Rikxoort, Eva, M1 aRousson, Mikael1 aRuskó, László1 aSaddi, Kinda, A1 aSchmidt, Günter1 aSeghers, Dieter1 aShimizu, Akinobi1 aSlagmolen, Pieter1 aSorantin, Erich1 aSoza, Grzegorz1 aSusomboon, Ruchaneewan1 aWaite, Jonathan, M1 aWimmer, Andreas1 aWolf, Ivo uhttps://www.inf.u-szeged.hu/en/publication/comparison-and-evaluation-of-methods-for-liver-segmentation-from-ct-datasets00682nas a2200157 4500008004100000245010800041210006900149260003800218490001800256100002000274700001800294700002800312700001700340700001600357856015100373 2009 eng d00aMethod and system for automatically segmenting organs from three dimensional computed tomography images0 aMethod and system for automatically segmenting organs from three aAmerikai Egyesült Államokc20090 vUS200509076901 aFidrich, Márta1 aMáté, Eörs1 aNyúl, László, Gábor1 aKuba, Attila1 aKiss, Bence uhttps://www.inf.u-szeged.hu/en/publication/method-and-system-for-automatically-segmenting-organs-from-three-dimensional-computed-tomography-images01187nas a2200193 4500008004100000020001400041245007700055210006900132260001300201300001400214490000700228520053400235100001900769700001800788700002800806700001700834700002000851856012200871 2008 eng d a0094-240500aGeometrical model-based segmentation of the organs of sight on CT images0 aGeometrical modelbased segmentation of the organs of sight on CT cFeb 2008 a735 - 7430 v353 aSegmentation 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.
1 aBekes, György1 aMáté, Eörs1 aNyúl, László, Gábor1 aKuba, Attila1 aFidrich, Márta uhttps://www.inf.u-szeged.hu/en/publication/geometrical-model-based-segmentation-of-the-organs-of-sight-on-ct-images-000673nas a2200181 4500008004100000245007300041210006900114260003800183490001800221100002000239700001700259700001800276700001800294700001700312700002800329700001800357856011600375 2008 eng d00aSystems and methods for segmenting an organ in a plurality of images0 aSystems and methods for segmenting an organ in a plurality of im aAmerikai Egyesült Államokc20080 vUS200408582411 aFidrich, Márta1 aMakay, Géza1 aMáté, Eörs1 aBalogh, Emese1 aKuba, Attila1 aNyúl, László, Gábor1 aKanyó, Judit uhttps://www.inf.u-szeged.hu/en/publication/systems-and-methods-for-segmenting-an-organ-in-a-plurality-of-images01508nas a2200193 4500008004100000020001400041245006400055210006300119260001400182300001400196490000600210520089000216100001901106700002801125700001801153700001701171700002001188856010601208 2007 eng d a1861-641000a3D segmentation of liver, kidneys and spleen from CT images0 a3D segmentation of liver kidneys and spleen from CT images cJune 2007 aS45 - S470 v23 aThe 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.
1 aBekes, György1 aNyúl, László, Gábor1 aMáté, Eörs1 aKuba, Attila1 aFidrich, Márta uhttps://www.inf.u-szeged.hu/en/publication/3d-segmentation-of-liver-kidneys-and-spleen-from-ct-images01308nas a2200109 4500008004100000245008400041210006900125260002500194300001400219520092000233856004501153 2005 eng d00aMethod for Automatically Segmenting the Spinal Cord and Canal from 3D CT Images0 aMethod for Automatically Segmenting the Spinal Cord and Canal fr aViennabOCGc2005/// a311 - 3183 aWe present two approaches for automatically segmenting thespinal cord/canal from native CT images of the thorax region containing the spine. Different strategies are included to handle images where only part of the spinal column is visible. The algorithms require one seed point given on a slice located in the middle region of the spine, and the rest is automatic. The spatial extent of the spinal cord/canal is determined automatically using anatomical information for segmenting the spinal canal while active contours are applied if the spinal cord is to be segmented. Both methods work in 2D and use propagated information from neighboring slices. They are also very rapid in execution, that means an efficient, user-friendly workflow. The methods were evaluated by radiologists and were found to be useful and met the accuracy and repeatability requirements for the particular task. uhttps://www.inf.u-szeged.hu/en/node/103000397nas a2200097 4500008004100000245008400041210006900125260004600194300001400240856004500254 2005 eng d00aMethod for automatically segmenting the spinal cord and canal from 3D CT images0 aMethod for automatically segmenting the spinal cord and canal fr aBerlin; HeidelbergbSpringer-Verlagc2005 a456 - 463 uhttps://www.inf.u-szeged.hu/en/node/1404