TY - CHAP AU - Serrano-Anton, Belen AU - Rehman, Mubashara AU - Martinel, Niki AU - Avanzo, Michele AU - Spizzo, Riccardo AU - Fanetti, Giuseppe AU - Munuzuri, Alberto P. AU - Micheloni, Christian ED - Antonacopoulos, A ED - Chaudhuri, S ED - Chellappa, R ED - Liu, CL ED - Bhattacharya, S ED - Pal, U TI - MAR-DTN: Metal Artifact Reduction Using Domain Transformation Network for Radiotherapy Planning T2 - PATTERN RECOGNITION, ICPR 2024, PT XI PB - Springer International Publishing CY - Cham SN - 9783031781957 T3 - Lecture Notes in Computer Science, ISSN 0302-9743 PY - 2025 SP - 143 EP - 159 PG - 17 DO - 10.1007/978-3-031-78195-7_10 UR - https://m2.mtmt.hu/api/publication/36684774 ID - 36684774 LA - English DB - MTMT ER - TY - JOUR AU - Selles, Mark AU - van Osch, Jochen A. C. AU - Maas, Mario AU - Boomsma, Martijn F. AU - Wellenberg, Ruud H. H. TI - Advances in metal artifact reduction in CT images: A review of traditional and novel metal artifact reduction techniques JF - EUROPEAN JOURNAL OF RADIOLOGY J2 - EUR J RADIOL VL - 170 PY - 2024 PG - 13 SN - 0720-048X DO - 10.1016/j.ejrad.2023.111276 UR - https://m2.mtmt.hu/api/publication/34640501 ID - 34640501 N1 - Department of Radiology, Isala, 8025 AB Zwolle, Netherlands Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, AZ Amsterdam, 1105, Netherlands Amsterdam Movement Sciences, BT Amsterdam, 1081, Netherlands Department of Medical Physics, Isala, 8025 AB Zwolle, Netherlands Cited By :12 Export Date: 16 November 2024 CODEN: EJRAD Correspondence Address: Selles, M.; Department of RadiologyNetherlands; email: m.selles@isala.nl LA - English DB - MTMT ER - TY - JOUR AU - Zhang, X. AU - Jiang, S. AU - Li, D. AU - Li, Z. AU - Yang, F. AU - Cheng, Y. AU - Zhu, X. AU - Pan, X. TI - The clinical value of spectral CT combined with orthopedic metal artifact reduction technology in reducing artifacts from contrast media in enhanced chest CT of breast cancer patients JF - ZHONGHUA FANGSHEXUE ZAZHI / CHINESE JOURNAL OF RADIOLOGY J2 - CHINESE J RADIOL VL - 57 PY - 2023 IS - 12 SP - 1353 EP - 1360 PG - 8 SN - 1005-1201 DO - 10.3760/cma.j.cn112149-20231007-00256 UR - https://m2.mtmt.hu/api/publication/35575326 ID - 35575326 N1 - West China School of Clinical Medicine, Sichuan University, Chengdu, 610041, China Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China Department of Pharmacy, West China Hospital, Sichuan University, Chengdu, 610041, China Export Date: 16 November 2024 Correspondence Address: Pan, X.; Department of Radiology, China; email: hx_pxl@163.com LA - Chinese DB - MTMT ER - TY - JOUR AU - Segota, Sandi Baressi AU - Lorencin, Ivan AU - Smolic, Klara AU - Andelic, Nikola AU - Markic, Dean AU - Mrzljak, Vedran AU - Stifanic, Daniel AU - Musulin, Jelena AU - Spanjol, Josip AU - Car, Zlatan TI - Semantic Segmentation of Urinary Bladder Cancer Masses from CT Images: A Transfer Learning Approach JF - BIOLOGY-BASEL J2 - BIOLOGY-BASEL VL - 10 PY - 2021 IS - 11 PG - 25 SN - 2079-7737 DO - 10.3390/biology10111134 UR - https://m2.mtmt.hu/api/publication/33436891 ID - 33436891 N1 - Faculty of Engineering, University of Rijeka, Vukovarska 58, Rijeka, 51000, Croatia Clinical Hospital Center Rijeka, Krešimirova 42, Rijeka, 51000, Croatia Faculty of Medicine, University of Rijeka, Branchetta 20/1, Rijeka, 51000, Croatia Cited By :7 Export Date: 16 November 2024 Correspondence Address: Mrzljak, V.; Faculty of Engineering, Vukovarska 58, Croatia; email: vmrzljak@riteh.hr AB - Simple Summary: Bladder cancer is a common cancer of the urinary tract, characterized by high metastatic potential and recurrence. The research applies a transfer learning approach on CT images (frontal, axial, and saggital axes) for the purpose of semantic segmentation of areas affected by bladder cancer. A system consisting of AlexNet network for plane recognition, using transfer learning-based U-net networks for the segmentation task. Achieved results show that the proposed system has a high performance, suggesting possible use in clinical practice.Abstract: Urinary bladder cancer is one of the most common cancers of the urinary tract. This cancer is characterized by its high metastatic potential and recurrence rate. Due to the high metastatic potential and recurrence rate, correct and timely diagnosis is crucial for successful treatment and care. With the aim of increasing diagnosis accuracy, artificial intelligence algorithms are introduced to clinical decision making and diagnostics. One of the standard procedures for bladder cancer diagnosis is computer tomography (CT) scanning. In this research, a transfer learning approach to the semantic segmentation of urinary bladder cancer masses from CT images is presented. The initial data set is divided into three sub-sets according to image planes: frontal (4413 images), axial (4993 images), and sagittal (996 images). First, AlexNet is utilized for the design of a plane recognition system, and it achieved high classification and generalization performances with an (AUC(micro)) over bar of 0.9999 and sigma(AUC(micro)) of 0.0006. Furthermore, by applying the transfer learning approach, significant improvements in both semantic segmentation and generalization performances were achieved. For the case of the frontal plane, the highest performances were achieved if pre-trained ResNet101 architecture was used as a backbone for U-net with (DSC) over bar up to 0.9587 and sigma(DSC) of 0.0059. When U-net was used for the semantic segmentation of urinary bladder cancer masses from images in the axial plane, the best results were achieved if pre-trained ResNet50 was used as a backbone, with a DSC up to 0.9372 and sigma(DSC) of 0.0147. Finally, in the case of images in the sagittal plane, the highest results were achieved with VGG-16 as a backbone. In this case, (DSC) over bar values up to 0.9660 with a sigma(DSC) of 0.0486 were achieved. From the listed results, the proposed semantic segmentation system worked with high performance both from the semantic segmentation and generalization standpoints. The presented results indicate that there is the possibility for the utilization of the semantic segmentation system in clinical practice. LA - English DB - MTMT ER -