TY - CONF T1 - Újszülöttek monitorozása képfolyam elemzéssel T2 - A XXVIII. Neumann Kollokvium konferencia-kiadványa Y1 - 2015 A1 - Jozsef Nemeth A1 - András Bánhalmi A1 - László G Nyúl A1 - Márta Fidrich A1 - Zsolt Szkiva A1 - Péter Franczia A1 - Csaba Berezki A1 - Vilmos Bilicki JF - A XXVIII. Neumann Kollokvium konferencia-kiadványa PB - Neumann János Számítógép-tudományi Társaság CY - Veszprém, Hungary SN - 978-615-5036-10-1 ER - TY - JOUR T1 - Comparison and evaluation of methods for liver segmentation from CT datasets JF - IEEE TRANSACTIONS ON MEDICAL IMAGING Y1 - 2009 A1 - Tobias Heimann A1 - Brahm Van Ginneken A1 - Martin A Styner A1 - Yulia Arzhaeva A1 - Volker Aurich A1 - Christian Bauer A1 - Andreas Beck A1 - Christoph Becker A1 - Reinhardt Beichel A1 - György Bekes A1 - Fernando Bello A1 - Gerd Binnig A1 - Horst Bischof A1 - Alexander Bornik A1 - Peter MM Cashman A1 - Ying Chi A1 - Andres Córdova A1 - Benoit M Dawant A1 - Márta Fidrich A1 - Jacob D Furst A1 - Daisuke Furukawa A1 - Lars Grenacher A1 - Joachim Hornegger A1 - Dagmar Kainmüller A1 - Richard I Kitney A1 - Hidefumi Kobatake A1 - Hans Lamecker A1 - Thomas Lange A1 - Jeongjin Lee A1 - Brian Lennon A1 - Rui Li A1 - Senhu Li A1 - Hans-Peter Meinzer A1 - Gábor Németh A1 - Daniela S Raicu A1 - Anne-Mareike Rau A1 - Eva M Van Rikxoort A1 - Mikael Rousson A1 - László Ruskó A1 - Kinda A Saddi A1 - Günter Schmidt A1 - Dieter Seghers A1 - Akinobi Shimizu A1 - Pieter Slagmolen A1 - Erich Sorantin A1 - Grzegorz Soza A1 - Ruchaneewan Susomboon A1 - Jonathan M Waite A1 - Andreas Wimmer A1 - Ivo Wolf AB -

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.

CY - 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 VL - 28 SN - 0278-0062 IS - 8 N1 - ScopusID: 68249121543doi: 10.1109/TMI.2009.2013851 JO - IEEE T MED IMAGING ER - TY - PAT T1 - Method and system for automatically segmenting organs from three dimensional computed tomography images Y1 - 2009 A1 - Márta Fidrich A1 - Eörs Máté A1 - László Gábor Nyúl A1 - Attila Kuba A1 - Bence Kiss CY - Amerikai Egyesült Államok VL - US20050907690 IS - US7545979 ER - TY - JOUR T1 - Geometrical model-based segmentation of the organs of sight on CT images JF - MEDICAL PHYSICS Y1 - 2008 A1 - György Bekes A1 - Eörs Máté A1 - László Gábor Nyúl A1 - Attila Kuba A1 - Márta Fidrich AB -

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.

VL - 35 SN - 0094-2405 IS - 2 N1 - UT: 000253318400036ScopusID: 38849194643doi: 10.1118/1.2826557 JO - MED PHYS ER - TY - PAT T1 - Systems and methods for segmenting an organ in a plurality of images Y1 - 2008 A1 - Márta Fidrich A1 - Géza Makay A1 - Eörs Máté A1 - Emese Balogh A1 - Attila Kuba A1 - László Gábor Nyúl A1 - Judit Kanyó CY - Amerikai Egyesült Államok VL - US20040858241 IS - US7388973 ER - TY - JOUR T1 - 3D segmentation of liver, kidneys and spleen from CT images JF - INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY Y1 - 2007 A1 - György Bekes A1 - László Gábor Nyúl A1 - Eörs Máté A1 - Attila Kuba A1 - Márta Fidrich AB -

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.

VL - 2 SN - 1861-6410 IS - 1 SUPPL. N1 - ScopusID: 34250685687doi: 10.1007/s11548-007-0083-7 JO - INT J COMPUT ASSIST RADIOL SURG ER - TY - CHAP T1 - Method for Automatically Segmenting the Spinal Cord and Canal from 3D CT Images T2 - Joint Hungarian-Austrian conference on image processing and pattern recognition. 5th conference of the Hungarian Association for Image Processing and Pattern Recognition (KÉPAF), 29th workshop of the Austrian Association for Pattern Reco Y1 - 2005 A1 - László Gábor Nyúl A1 - Judit Kanyó A1 - Eörs Máté A1 - Géza Makay A1 - Emese Balogh A1 - Márta Fidrich A1 - Attila Kuba ED - Dmitrij Chetverikov ED - László Czúni ED - Markus Vincze AB - We 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. JF - Joint Hungarian-Austrian conference on image processing and pattern recognition. 5th conference of the Hungarian Association for Image Processing and Pattern Recognition (KÉPAF), 29th workshop of the Austrian Association for Pattern Reco PB - OCG CY - Vienna ER - TY - CHAP T1 - Method for automatically segmenting the spinal cord and canal from 3D CT images T2 - Computer Analysis of Images and Patterns Y1 - 2005 A1 - László Gábor Nyúl A1 - Judit Kanyó A1 - Eörs Máté A1 - Géza Makay A1 - Emese Balogh A1 - Márta Fidrich A1 - Attila Kuba ED - André Gagalowitz ED - Wilfried Philips JF - Computer Analysis of Images and Patterns PB - Springer-Verlag CY - Berlin; Heidelberg N1 - UT: 000232301200056 ER -