@inproceedings{MTMT:36684774,
	title = {MAR-DTN: Metal Artifact Reduction Using Domain Transformation Network for Radiotherapy Planning},
	url = {https://m2.mtmt.hu/api/publication/36684774},
	author = {Serrano-Anton, Belen and Rehman, Mubashara and Martinel, Niki and Avanzo, Michele and Spizzo, Riccardo and Fanetti, Giuseppe and Munuzuri, Alberto P. and Micheloni, Christian},
	booktitle = {PATTERN RECOGNITION, ICPR 2024, PT XI},
	doi = {10.1007/978-3-031-78195-7_10},
	unique-id = {36684774},
	keywords = {ARTIFICIAL INTELLIGENCE (AI); metal artifact reduction (MAR); kilo-Voltage-CT (kVCT); Mega-Voltage-CT (MVCT)},
	year = {2025},
	pages = {143-159}
}

@article{MTMT:34640501,
	title = {Advances in metal artifact reduction in CT images: A review of traditional and novel metal artifact reduction techniques},
	url = {https://m2.mtmt.hu/api/publication/34640501},
	author = {Selles, Mark and van Osch, Jochen A. C. and Maas, Mario and Boomsma, Martijn F. and Wellenberg, Ruud H. H.},
	doi = {10.1016/j.ejrad.2023.111276},
	journal-iso = {EUR J RADIOL},
	journal = {EUROPEAN JOURNAL OF RADIOLOGY},
	volume = {170},
	unique-id = {34640501},
	issn = {0720-048X},
	keywords = {Artificial intelligence; CT; METAL ARTIFACT REDUCTION; Dual energy CT; photon counting CT},
	year = {2024},
	eissn = {1872-7727},
	orcid-numbers = {Selles, Mark/0000-0002-1511-1438}
}

@article{MTMT:35575326,
	title = {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},
	url = {https://m2.mtmt.hu/api/publication/35575326},
	author = {Zhang, X. and Jiang, S. and Li, D. and Li, Z. and Yang, F. and Cheng, Y. and Zhu, X. and Pan, X.},
	doi = {10.3760/cma.j.cn112149-20231007-00256},
	journal-iso = {CHINESE J RADIOL},
	journal = {ZHONGHUA FANGSHEXUE ZAZHI / CHINESE JOURNAL OF RADIOLOGY},
	volume = {57},
	unique-id = {35575326},
	issn = {1005-1201},
	year = {2023},
	pages = {1353-1360}
}

@article{MTMT:33436891,
	title = {Semantic Segmentation of Urinary Bladder Cancer Masses from CT Images: A Transfer Learning Approach},
	url = {https://m2.mtmt.hu/api/publication/33436891},
	author = {Segota, Sandi Baressi and Lorencin, Ivan and Smolic, Klara and Andelic, Nikola and Markic, Dean and Mrzljak, Vedran and Stifanic, Daniel and Musulin, Jelena and Spanjol, Josip and Car, Zlatan},
	doi = {10.3390/biology10111134},
	journal-iso = {BIOLOGY-BASEL},
	journal = {BIOLOGY-BASEL},
	volume = {10},
	unique-id = {33436891},
	abstract = {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.},
	keywords = {Artificial intelligence; computer tomography; machine learning; Semantic segmentation; urinary bladder cancer},
	year = {2021},
	eissn = {2079-7737},
	orcid-numbers = {Lorencin, Ivan/0000-0002-5964-245X; Smolic, Klara/0000-0001-9704-4314; Markic, Dean/0000-0001-5696-0850; Mrzljak, Vedran/0000-0003-0323-2600; Spanjol, Josip/0000-0002-5551-8128; Car, Zlatan/0000-0003-2817-9252}
}