@article{MTMT:34145498, title = {Comprehensive deep learning-based framework for automatic organs-at-risk segmentation in head-and-neck and pelvis for MR-guided radiation therapy planning}, url = {https://m2.mtmt.hu/api/publication/34145498}, author = {Czipczer, Vanda and Kolozsvári, Bernadett and Deák-Karancsi, Borbála and E. Capala, Marta and A. Pearson, Rachel and Borzási, Emőke and Együd, Zsófia and Gaál, Szilvia and Kelemen, Gyöngyi and Kószó, Renáta Lilla and Paczona, Viktor Róbert and Végváry, Zoltán and Karancsi, Zsófia and Kékesi, Ádám and Czunyi, Edina and H. Irmai, Blanka and G.Keresnyei, Nóra and Nagypál, Petra and Czabány, Renáta and Gyalai, Bence and P. Tass, Bulcsú and Cziria, Balázs and Cozzini, Cristina and Estkowsky, Lloyd and Ferenczi, Lehel and Frontó, András and Maxwell, Ross and Megyeri, István and Mian, Michael and Tan, Tao and Wyatt, Jonathan and Wiesinger, Florian and Hideghéty, Katalin and McCallum, Hazel and F. Petit, Steven and Ruskó, László}, doi = {10.3389/fphy.2023.1236792}, journal-iso = {FRONT PHYS-LAUSANNE}, journal = {FRONTIERS IN PHYSICS}, volume = {11}, unique-id = {34145498}, year = {2023}, eissn = {2296-424X}, orcid-numbers = {Kószó, Renáta Lilla/0000-0002-1958-7839; Nagypál, Petra/0009-0002-9663-0666; Hideghéty, Katalin/0000-0001-7080-2365} } @article{MTMT:33033365, title = {Magnetic Resonance Imaging−Based Delineation of Organs at Risk in the Head and Neck Region}, url = {https://m2.mtmt.hu/api/publication/33033365}, author = {Paczona, Viktor Róbert and Capala, Marta E. and Deák-Karancsi, Borbála and Borzási, Emőke and Együd, Zsófia and Végváry, Zoltán and Kelemen, Gyöngyi and Kószó, Renáta Lilla and Ruskó, László and Ferenczi, Lehel and Verduijn, Gerda M. and Petit, Steven F. and Oláh, Judit Magdolna and Cserháti, Adrienn and Wiesinger, Florian and Hideghéty, Katalin}, doi = {10.1016/j.adro.2022.101042}, journal-iso = {Advances in Radiation Oncology}, journal = {Advances in Radiation Oncology}, volume = {8}, unique-id = {33033365}, issn = {2452-1094}, year = {2022}, orcid-numbers = {Kószó, Renáta Lilla/0000-0002-1958-7839; Hideghéty, Katalin/0000-0001-7080-2365} } @article{MTMT:33084927, title = {Automated organ at risk delineation in T2w head and pelvis MR images for MR-only radiation therapy}, url = {https://m2.mtmt.hu/api/publication/33084927}, author = {Ruskó, László and Czipczer, Vanda and Kolozsvári, B. and Deák-Karancsi, Borbála and Czabány, R. and Gyalai, B. and Hajnal, D. and Karancsi, Zsófia and Capala, M.E. and Verduijn, G.M. and Pearson, R. and Wyatt, J.J. and Borzási, Emőke and Kelemen, Gyöngyi and Kószó, Renáta Lilla and Paczona, Viktor Róbert and Végváry, Zoltán and Cozzini, C. and Tan, T. and Maxwell, R. and Hernandez Tamames, J.A. and Petit, S.F. and Mccallum, H. and Hideghéty, Katalin and Wiesinger, F.}, doi = {10.1016/S0167-8140(21)06787-6}, journal-iso = {RADIOTHER ONCOL}, journal = {RADIOTHERAPY AND ONCOLOGY}, volume = {161}, unique-id = {33084927}, issn = {0167-8140}, year = {2021}, eissn = {1879-0887}, pages = {S67-S68}, orcid-numbers = {Kószó, Renáta Lilla/0000-0002-1958-7839; Hideghéty, Katalin/0000-0001-7080-2365} } @inproceedings{MTMT:31926981, title = {Deep-Learning-based Segmentation of Organs-at-Risk in the Head for MR-assisted Radiation Therapy Planning}, url = {https://m2.mtmt.hu/api/publication/31926981}, author = {Ruskó, László and Capala, Marta and Czipczer, Vanda and Kolozsvári, Bernadett and Deák-Karancsi, Borbála and Czabány, Renáta and Gyalai, Bence and Tan, Tao and Végváry, Zoltán and Borzási, Emőke and Együd, Zsófia and Kószó, Renáta Lilla and Paczona, Viktor Róbert and Fodor, Emese and Bobb, Chad and Cozzini, Cristina and Kaushik, Sandeep and Darázs, Barbara and Verduijn, Gerda M. and Pearson, Rachel and Maxwell, Ross and Mccallum, Hazel and Hernandez Tamames, Juan and Hideghéty, Katalin and Petit, Steven and Wiesinger, Florian}, booktitle = {Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies}, doi = {10.5220/0010235000002865}, unique-id = {31926981}, year = {2021}, pages = {31-43}, orcid-numbers = {Kószó, Renáta Lilla/0000-0002-1958-7839; Fodor, Emese/0009-0008-6961-6731; Hideghéty, Katalin/0000-0001-7080-2365} } @article{MTMT:31040522, title = {Automatic Recognition of Anatomical Regions in Computed Tomography Images}, url = {https://m2.mtmt.hu/api/publication/31040522}, author = {Tóth, Márton József and Ruskó, László and Csébfalvi, Balázs}, doi = {10.3311/PPee.12899}, journal-iso = {PERIOD POLYTECH ELECTR ENG COMP SCI}, journal = {PERIODICA POLYTECHNICA-ELECTRICAL ENGINEERING AND COMPUTER SCIENCE}, volume = {62}, unique-id = {31040522}, issn = {2064-5260}, year = {2018}, eissn = {2064-5279}, pages = {117-125} } @mastersthesis{MTMT:2898067, title = {Automated segmentation methods for liver analysis in oncology applications}, url = {https://m2.mtmt.hu/api/publication/2898067}, author = {Ruskó, László}, doi = {10.14232/phd.2044}, publisher = {SZTE}, unique-id = {2898067}, year = {2014} } @article{MTMT:3271585, title = {Májdaganatok automatikus CT-alapú preoperatív helyzetmeghatározása [CT-based automatic preoperative localization of liver tumors]}, url = {https://m2.mtmt.hu/api/publication/3271585}, author = {Urbán, Olga and Ruskó, László and Mátéka, Ilona and Palkó, András}, journal-iso = {MAGYAR RADIOLÓGIA ONLINE}, journal = {MAGYAR RADIOLÓGIA ONLINE}, volume = {4}, unique-id = {3271585}, year = {2013}, eissn = {2063-9481}, orcid-numbers = {Palkó, András/0000-0002-5370-6934} } @article{MTMT:2516477, title = {Virtual volume resection using multi-resolution triangular representation of B-spline surfaces}, url = {https://m2.mtmt.hu/api/publication/2516477}, author = {Ruskó, László and Mateka, I and Kriston, András}, doi = {10.1016/j.cmpb.2013.04.017}, journal-iso = {COMPUT METH PROG BIO}, journal = {COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE}, volume = {111}, unique-id = {2516477}, issn = {0169-2607}, abstract = {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.}, year = {2013}, eissn = {1872-7565}, pages = {315-329} } @article{MTMT:2516476, title = {Automated liver lesion detection in CT images based on multi-level geometric features.}, url = {https://m2.mtmt.hu/api/publication/2516476}, author = {Ruskó, László and Perenyi, A}, doi = {10.1007/s11548-013-0949-9}, journal-iso = {INT J COMPUT ASSIST RADIOL SURG}, journal = {INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY}, volume = {9}, unique-id = {2516476}, issn = {1861-6410}, abstract = {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.}, year = {2013}, eissn = {1861-6429}, pages = {577-593} } @book{MTMT:2251575, title = {Systems, apparatus and processes for automated medical image segmentation using a statistical model}, url = {https://m2.mtmt.hu/api/publication/2251575}, author = {Márta, Fidrich and Ruskó, László and György, Bekes}, unique-id = {2251575}, year = {2013} }