TY - JOUR AU - Czipczer, Vanda AU - Kolozsvári, Bernadett AU - Deák-Karancsi, Borbála AU - E. Capala, Marta AU - A. Pearson, Rachel AU - Borzási, Emőke AU - Együd, Zsófia AU - Gaál, Szilvia AU - Kelemen, Gyöngyi AU - Kószó, Renáta Lilla AU - Paczona, Viktor Róbert AU - Végváry, Zoltán AU - Karancsi, Zsófia AU - Kékesi, Ádám AU - Czunyi, Edina AU - H. Irmai, Blanka AU - G.Keresnyei, Nóra AU - Nagypál, Petra AU - Czabány, Renáta AU - Gyalai, Bence AU - P. Tass, Bulcsú AU - Cziria, Balázs AU - Cozzini, Cristina AU - Estkowsky, Lloyd AU - Ferenczi, Lehel AU - Frontó, András AU - Maxwell, Ross AU - Megyeri, István AU - Mian, Michael AU - Tan, Tao AU - Wyatt, Jonathan AU - Wiesinger, Florian AU - Hideghéty, Katalin AU - McCallum, Hazel AU - F. Petit, Steven AU - Ruskó, László TI - Comprehensive deep learning-based framework for automatic organs-at-risk segmentation in head-and-neck and pelvis for MR-guided radiation therapy planning JF - FRONTIERS IN PHYSICS J2 - FRONT PHYS-LAUSANNE VL - 11 PY - 2023 PG - 17 SN - 2296-424X DO - 10.3389/fphy.2023.1236792 UR - https://m2.mtmt.hu/api/publication/34145498 ID - 34145498 LA - English DB - MTMT ER - TY - JOUR AU - Paczona, Viktor Róbert AU - Capala, Marta E. AU - Deák-Karancsi, Borbála AU - Borzási, Emőke AU - Együd, Zsófia AU - Végváry, Zoltán AU - Kelemen, Gyöngyi AU - Kószó, Renáta Lilla AU - Ruskó, László AU - Ferenczi, Lehel AU - Verduijn, Gerda M. AU - Petit, Steven F. AU - Oláh, Judit Magdolna AU - Cserháti, Adrienn AU - Wiesinger, Florian AU - Hideghéty, Katalin TI - Magnetic Resonance Imaging−Based Delineation of Organs at Risk in the Head and Neck Region JF - Advances in Radiation Oncology J2 - Advances in Radiation Oncology VL - 8 PY - 2022 IS - 2 PG - 10 SN - 2452-1094 DO - 10.1016/j.adro.2022.101042 UR - https://m2.mtmt.hu/api/publication/33033365 ID - 33033365 LA - English DB - MTMT ER - TY - JOUR AU - Ruskó, László AU - Czipczer, Vanda AU - Kolozsvári, B. AU - Deák-Karancsi, Borbála AU - Czabány, R. AU - Gyalai, B. AU - Hajnal, D. AU - Karancsi, Zsófia AU - Capala, M.E. AU - Verduijn, G.M. AU - Pearson, R. AU - Wyatt, J.J. AU - Borzási, Emőke AU - Kelemen, Gyöngyi AU - Kószó, Renáta Lilla AU - Paczona, Viktor Róbert AU - Végváry, Zoltán AU - Cozzini, C. AU - Tan, T. AU - Maxwell, R. AU - Hernandez Tamames, J.A. AU - Petit, S.F. AU - Mccallum, H. AU - Hideghéty, Katalin AU - Wiesinger, F. TI - Automated organ at risk delineation in T2w head and pelvis MR images for MR-only radiation therapy JF - RADIOTHERAPY AND ONCOLOGY J2 - RADIOTHER ONCOL VL - 161 PY - 2021 IS - Suppl. 1 SP - S67 EP - S68 PG - 2 SN - 0167-8140 DO - 10.1016/S0167-8140(21)06787-6 UR - https://m2.mtmt.hu/api/publication/33084927 ID - 33084927 LA - English DB - MTMT ER - TY - CHAP AU - Ruskó, László AU - Capala, Marta AU - Czipczer, Vanda AU - Kolozsvári, Bernadett AU - Deák-Karancsi, Borbála AU - Czabány, Renáta AU - Gyalai, Bence AU - Tan, Tao AU - Végváry, Zoltán AU - Borzási, Emőke AU - Együd, Zsófia AU - Kószó, Renáta Lilla AU - Paczona, Viktor Róbert AU - Fodor, Emese AU - Bobb, Chad AU - Cozzini, Cristina AU - Kaushik, Sandeep AU - Darázs, Barbara AU - Verduijn, Gerda M. AU - Pearson, Rachel AU - Maxwell, Ross AU - Mccallum, Hazel AU - Hernandez Tamames, Juan AU - Hideghéty, Katalin AU - Petit, Steven AU - Wiesinger, Florian ED - Douplik, A ED - Fred, A ED - Gamboa, H TI - Deep-Learning-based Segmentation of Organs-at-Risk in the Head for MR-assisted Radiation Therapy Planning T2 - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies PB - SciTePress CY - Setubal T3 - Biostec, ISSN 2184-4305 ; 2. PY - 2021 SP - 31 EP - 43 PG - 13 DO - 10.5220/0010235000002865 UR - https://m2.mtmt.hu/api/publication/31926981 ID - 31926981 N1 - Funding Agency and Grant Number: EIT Health [19037, 20648]; European Institute of Innovation and Technology (EIT); European Union's Horizon 2020 Research and innovation programme Funding text: This research is part of the Deep MR-only Radiation Therapy activity (project numbers: 19037, 20648) that has received funding from EIT Health. EIT Health is supported by the European Institute of Innovation and Technology (EIT), a body of the European Union receives support from the European Union's Horizon 2020 Research and innovation programme. LA - English DB - MTMT ER - TY - JOUR AU - Tóth, Márton József AU - Ruskó, László AU - Csébfalvi, Balázs TI - Automatic Recognition of Anatomical Regions in Computed Tomography Images JF - PERIODICA POLYTECHNICA-ELECTRICAL ENGINEERING AND COMPUTER SCIENCE J2 - PERIOD POLYTECH ELECTR ENG COMP SCI VL - 62 PY - 2018 IS - 4 SP - 117 EP - 125 PG - 9 SN - 2064-5260 DO - 10.3311/PPee.12899 UR - https://m2.mtmt.hu/api/publication/31040522 ID - 31040522 LA - English DB - MTMT ER - TY - THES AU - Ruskó, László TI - Automated segmentation methods for liver analysis in oncology applications PB - Szegedi Tudományegyetem (SZTE) PY - 2014 SP - 135 DO - 10.14232/phd.2044 UR - https://m2.mtmt.hu/api/publication/2898067 ID - 2898067 LA - English DB - MTMT ER - TY - JOUR AU - Urbán, Olga AU - Ruskó, László AU - Mátéka, Ilona AU - Palkó, András TI - Májdaganatok automatikus CT-alapú preoperatív helyzetmeghatározása [CT-based automatic preoperative localization of liver tumors] JF - MAGYAR RADIOLÓGIA ONLINE J2 - MAGYAR RADIOLÓGIA ONLINE VL - 4 PY - 2013 IS - 5 PG - 12 SN - 2063-9481 UR - https://m2.mtmt.hu/api/publication/3271585 ID - 3271585 LA - Hungarian DB - MTMT ER - TY - JOUR AU - Ruskó, László AU - Mateka, I AU - Kriston, András TI - Virtual volume resection using multi-resolution triangular representation of B-spline surfaces JF - COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE J2 - COMPUT METH PROG BIO VL - 111 PY - 2013 IS - 2 SP - 315 EP - 329 PG - 15 SN - 0169-2607 DO - 10.1016/j.cmpb.2013.04.017 UR - https://m2.mtmt.hu/api/publication/2516477 ID - 2516477 AB - Computer assisted analysis of organs has an important role in clinical diagnosis and therapy planning. As well as the visualization, the manipulation of 3-dimensional (3D) objects are key features of medical image processing tools. The goal of this work was to develop an efficient and easy to use tool that allows the physician to partition a segmented organ into its segments or lobes. The proposed tool allows the user to define a cutting surface by drawing some traces on 2D sections of a 3D object, cut the object into two pieces with a smooth surface that fits the input traces, and iterate the process until the object is partitioned at the desired level. The tool is based on an algorithm that interpolates the user-defined traces with B-spline surface and computes a binary cutting volume that represents the different sides of the surface. The computation of the cutting volume is based on the multi-resolution triangulation of the B-spline surface. The proposed algorithm was integrated into an open-source medical image processing framework. Using the tool, the user can select the object to be partitioned (e.g. segmented liver), define the cutting surface based on the corresponding medical image (medical image visualizing the internal structure of the liver), cut the selected object, and iterate the process. In case of liver segment separation, the cuts can be performed according to a predefined sequence, which makes it possible to label the temporary as well as the final partitions (lobes, segments) automatically. The presented tool was evaluated for anatomical segment separation of the liver involving 14 cases and virtual liver tumor resection involving one case. The segment separation was repeated 3 different times by one physician for all cases, and the average and the standard deviation of segment volumes were computed. According to the test experiences the presented algorithm proved to be efficient and user-friendly enough to perform free form cuts for liver segment separation and virtual liver tumor resection. The volume quantification of segments showed good correlation with the prior art and the vessel-based liver segment separation, which demonstrate the clinical usability of the presented method. LA - English DB - MTMT ER - TY - JOUR AU - Ruskó, László AU - Perenyi, A TI - Automated liver lesion detection in CT images based on multi-level geometric features. JF - INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY J2 - INT J COMPUT ASSIST RADIOL SURG VL - 9 PY - 2013 IS - 4 SP - 577 EP - 593 PG - 17 SN - 1861-6410 DO - 10.1007/s11548-013-0949-9 UR - https://m2.mtmt.hu/api/publication/2516476 ID - 2516476 AB - PURPOSE: Due to the increasing number of liver cancer cases in clinical practice, there is a significant need for efficient tools for computer-assisted liver lesion analysis. A wide range of clinical applications, such as lesion characterization, quantification and follow-up, can be facilitated by automated liver lesion detection. Liver lesions vary significantly in size, shape, density and heterogeneity, which make them difficult to detect automatically. The goal of this work was to develop a method that can detect all types of liver lesions with high sensitivity and low false positive rate within a short run time. METHODS: The proposed method identifies abnormal regions in liver CT images based on their intensity using a multi-level segmentation approach. The abnormal regions are analyzed from the inside-out using basic geometric features (such as asymmetry, compactness or volume). Using this multi-level shape characterization, the abnormal regions are classified into lesions and other region types (including vessel, liver boundary). The proposed analysis also allows defining the contour of each finding. The method was trained on a set of 55 cases involving 120 lesions and evaluated on a set of 30 images involving 59 (various types of) lesions, which were manually contoured by a physician. RESULTS: The proposed algorithm demonstrated a high detection rate (92 %) at a low (1.7) false positive per case (precision 51 %), when the method was started from a manually contoured liver. The same level of false positive per case (1.6) and precision (51 %) was achieved at a somewhat lower detection rate (85 %), when the volume of interest was defined by a fully automated liver segmentation. CONCLUSIONS: The proposed method can efficiently detect liver lesions irrespective of their size, shape, density and heterogeneity within half a minute. According to the evaluation, its accuracy is competitive with the actual state-of-the-art approaches, and the contour of the detected findings is acceptable in most of the cases. Future work shall focus on more precise lesion contouring so that the proposed method can be a solid basis for fully automated liver tumour burden estimation. LA - English DB - MTMT ER - TY - PAT AU - Márta, Fidrich AU - Ruskó, László AU - György, Bekes TI - Systems, apparatus and processes for automated medical image segmentation using a statistical model PY - 2013 UR - https://m2.mtmt.hu/api/publication/2251575 ID - 2251575 LA - English DB - MTMT ER -