@article{MTMT:34110644,
	title = {Targeting Melanoma-Associated Fibroblasts (MAFs) with Activated γδ (Vδ2) T Cells: An In Vitro Cytotoxicity Model},
	url = {https://m2.mtmt.hu/api/publication/34110644},
	author = {Hajdara, Anna and Cakir, Ugur and Molnár-Érsek, Barbara and Silló, Pálma and Széky, Balázs and Barna, Gábor and Faqi, Shaaban and Gyöngy, Miklós and Kárpáti, Sarolta and Németh, Krisztián and Mayer, Balázs},
	doi = {10.3390/ijms241612893},
	journal-iso = {INT J MOL SCI},
	journal = {INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES},
	volume = {24},
	unique-id = {34110644},
	issn = {1661-6596},
	abstract = {The tumor microenvironment (TME) has gained considerable scientific attention by playing a role in immunosuppression and tumorigenesis. Besides tumor cells, TME is composed of various other cell types, including cancer-associated fibroblasts (CAFs or MAFs when referring to melanoma-derived CAFs) and tumor-infiltrating lymphocytes (TILs), a subpopulation of which is labeled as γδ T cells. Since the current anti-cancer therapies using γδ T cells in various cancers have exhibited mixed treatment responses, to better understand the γδ T cell biology in melanoma, our research group aimed to investigate whether activated γδ T cells are capable of killing MAFs. To answer this question, we set up an in vitro platform using freshly isolated Vδ2-type γδ T cells and cultured MAFs that were biobanked from our melanoma patients. This study proved that the addition of zoledronic acid (1–2.5 µM) to the γδ T cells was necessary to drive MAFs into apoptosis. The MAF cytotoxicity of γδ T cells was further enhanced by using the stimulatory clone 20.1 of anti-BTN3A1 antibody but was reduced when anti-TCR γδ or anti-BTN2A1 antibodies were used. Since the administration of zoledronic acid is safe and tolerable in humans, our results provide further data for future clinical studies on the treatment of melanoma.},
	year = {2023},
	eissn = {1422-0067},
	orcid-numbers = {Cakir, Ugur/0000-0001-8270-1430; Molnár-Érsek, Barbara/0000-0001-8627-9601; Silló, Pálma/0000-0002-6940-8368; Barna, Gábor/0000-0003-1960-5061; Kárpáti, Sarolta/0000-0002-8472-0712; Mayer, Balázs/0000-0003-3577-3823}
}

@inproceedings{MTMT:32249821,
	title = {Single Image Super-Resolution Of Noisy 3d Dental Ct Images Using Tucker Decomposition},
	url = {https://m2.mtmt.hu/api/publication/32249821},
	author = {Hatvani, Janka and Michetti, J. and Basarab, A. and Gyöngy, Miklós and Kouame, D.},
	booktitle = {18th IEEE International Symposium on Biomedical Imaging, ISBI 2021},
	doi = {10.1109/ISBI48211.2021.9433999},
	unique-id = {32249821},
	year = {2021},
	pages = {1673-1676}
}

@article{MTMT:32125812,
	title = {Automated Skin Lesion Classification on Ultrasound Images},
	url = {https://m2.mtmt.hu/api/publication/32125812},
	author = {Marosán-Vilimszky, Péter and Szalai, Klára and Horváth, András and Csabai, Domonkos and Fuzesi, Krisztian and Csány, Gergely and Gyöngy, Miklós},
	doi = {10.3390/diagnostics11071207},
	journal-iso = {DIAGNOSTICS},
	journal = {DIAGNOSTICS},
	volume = {11},
	unique-id = {32125812},
	issn = {2075-4418},
	abstract = {The growing incidence of skin cancer makes computer-aided diagnosis tools for this group of diseases increasingly important. The use of ultrasound has the potential to complement information from optical dermoscopy. The current work presents a fully automatic classification framework utilizing fully-automated (FA) segmentation and compares it with classification using two semi-automated (SA) segmentation methods. Ultrasound recordings were taken from a total of 310 lesions (70 melanoma, 130 basal cell carcinoma and 110 benign nevi). A support vector machine (SVM) model was trained on 62 features, with ten-fold cross-validation. Six classification tasks were considered, namely all the possible permutations of one class versus one or two remaining classes. The receiver operating characteristic (ROC) area under the curve (AUC) as well as the accuracy (ACC) were measured. The best classification was obtained for the classification of nevi from cancerous lesions (melanoma, basal cell carcinoma), with AUCs of over 90% and ACCs of over 85% obtained with all segmentation methods. Previous works have either not implemented FA ultrasound-based skin cancer classification (making diagnosis more lengthy and operator-dependent), or are unclear in their classification results. Furthermore, the current work is the first to assess the effect of implementing FA instead of SA classification, with FA classification never degrading performance (in terms of AUC or ACC) by more than 5%.},
	keywords = {TUMORS; texture; DIFFERENTIAL-DIAGNOSIS; Computer vision; BENIGN; COMPUTER-AIDED DIAGNOSIS; COMPUTER-AIDED DIAGNOSIS; HIGH-FREQUENCY ULTRASOUND; skin ultrasound; BREAST-LESIONS; TRANSFORM FEATURES; SCAN ULTRASOUND},
	year = {2021},
	eissn = {2075-4418}
}

@article{MTMT:31810968,
	title = {Automated seeding for ultrasound skin lesion segmentation},
	url = {https://m2.mtmt.hu/api/publication/31810968},
	author = {Marosán-Vilimszky, Péter and Szalai, Klára and Csabai, Domonkos and Csány, Gergely and Horváth, András and Gyöngy, Miklós},
	doi = {10.1016/j.ultras.2020.106268},
	journal-iso = {ULTRASONICS},
	journal = {ULTRASONICS},
	volume = {110},
	unique-id = {31810968},
	issn = {0041-624X},
	abstract = {The segmentation of cancer-suspicious skin lesions using ultrasound may help their differential diagnosis and treatment planning. Active contour models (ACM) require an initial seed, which when manually chosen may cause variations in segmentation accuracy. Fully-automated skin segmentation typically employs layer-by-layer segmentation using a combination of methods; however, such segmentation has not yet been applied on cancerous lesions. In the current work, fully automated segmentation is achieved in two steps: an automated seeding (AS) step using a layer-by-layer method followed by a growing step using an ACM. The method was tested on images of nevi, melanomas, and basal cell carcinomas from two ultrasound imaging systems (N = 60), with all lesions being successfully located. For the seeding step, manual seeding (MS) was used as a reference. AS approached the accuracy of MS when the latter used an optimal bounding rectangle based on the ground truth (Sorensen-Dice coefficient (SDC) of 72.3 vs 74.6, respectively). The effect of varying the manual seed was also investigated; a 0.7 decrease in seed height and width caused a mean SDC of 54.6. The results show the robustness of automated seeding for skin lesion segmentation.},
	keywords = {DIFFERENTIAL-DIAGNOSIS; Computer vision; IMAGES; Acoustics; skin ultrasound; Automated layer segmentation; Automated lesion localization},
	year = {2021},
	eissn = {1874-9968}
}

@article{MTMT:31289186,
	title = {Estimation of Acoustic Power Output from Electrical Impedance Measurements},
	url = {https://m2.mtmt.hu/api/publication/31289186},
	author = {Csány, Gergely and Gray, Michael D. and Gyöngy, Miklós},
	doi = {10.3390/acoustics2010004},
	journal-iso = {Acoustics},
	journal = {Acoustics},
	volume = {2},
	unique-id = {31289186},
	year = {2020},
	eissn = {2624-599X},
	pages = {37-50}
}

@article{MTMT:30844179,
	title = {Enhancement of Acoustic Microscopy Lateral Resolution: A Comparison Between Deep Learning and Two Deconvolution Methods},
	url = {https://m2.mtmt.hu/api/publication/30844179},
	author = {Makra, Ákos and Bost, Wolfgang and Kalló, Imre and Horváth, András and Fournelle, Marc and Gyöngy, Miklós},
	doi = {10.1109/TUFFC.2019.2940003},
	journal-iso = {IEEE T ULTRASON FERR},
	journal = {IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL},
	volume = {67},
	unique-id = {30844179},
	issn = {0885-3010},
	year = {2020},
	eissn = {1525-8955},
	pages = {136-145}
}

@article{MTMT:31046652,
	title = {A real-time data-based scan conversion method for single element ultrasound transducers},
	url = {https://m2.mtmt.hu/api/publication/31046652},
	author = {Csány, Gergely and Szalai, Klára and Gyöngy, Miklós},
	doi = {10.1016/j.ultras.2018.10.006},
	journal-iso = {ULTRASONICS},
	journal = {ULTRASONICS},
	volume = {93},
	unique-id = {31046652},
	issn = {0041-624X},
	abstract = {The current work investigates the performance of a real-time scan conversion algorithm for generating a 2-D ultrasound image from a laterally scanned single-element ultrasound transducer, which has applications in point-of-care devices such as for skin imaging. The algorithm employs a fixed calibration curve to update a predefined image grid in real time. Simulations showed that the calibration curve (with a maximum of 1) is robust to changes in scatterer concentration (8.3 x 10(-3) mean absolute error), signal to noise ratio (1.0 x 10(-3) mean absolute error for -5 dB SNR), and can be accurately predicted from a small number (31) of point scatterers (6.9 x 10(-3) mean absolute error). Good agreement was also found between the calibration curves obtained from simulated and experimental data (1.19 x 10(-2) mean absolute error). The scan conversion algorithm was validated by evaluation of the position estimation errors on both simulations and experiments. Clinical images of skin lesions (N = 20) demonstrate the feasibility of the algorithm for real, non-homogeneous tissue. Use of a fixed calibration curve compared to an adaptive calibration curve gave similar accuracies in the scanning step size range of 150-350 mu m (with an average overlap of the accuracy ranges of 92.94% for simulations and 42.83% for experiments), and a 350-fold improvement in computation time.},
	keywords = {Correlation; Single-element transducer; Image formation; Freehand scanning; Scan conversion},
	year = {2019},
	eissn = {1874-9968},
	pages = {26-36}
}

@inproceedings{MTMT:30870998,
	title = {Tensor-Factorization-Based 3d Single Image Super-Resolution with Semi-Blind Point Spread Function Estimation},
	url = {https://m2.mtmt.hu/api/publication/30870998},
	author = {Hatvani, Janka and Basarab, A. and Michetti, J. and Gyöngy, Miklós and Kouame, D.},
	booktitle = {2019 IEEE International Conference on Image Processing (ICIP)},
	doi = {10.1109/ICIP.2019.8803354},
	unique-id = {30870998},
	abstract = {A volumetric non-blind single image super-resolution technique using tensor factorization has been recently introduced by our group. That method allowed a 2-order-of-magnitude faster high-resolution image reconstruction with equivalent image quality compared to state-of-the-art algorithms. In this work a joint alternating recovery of the high-resolution image and of the unknown point spread function parameters is proposed. The method is evaluated on dental computed to-mography images. The algorithm was compared to an existing 3D super-resolution method using low-rank and total variation regularization, combined with the same alternating PSF-optimization. The two algorithms have shown similar improvement in PSNR, but our method converged roughly 40 times faster, under 6 minutes both in simulation and on experimental dental computed tomography data.},
	year = {2019},
	pages = {2871-2875}
}

@article{MTMT:30395908,
	title = {A low-cost portable ultrasound system for skin diagnosis},
	url = {https://m2.mtmt.hu/api/publication/30395908},
	author = {Csány, Gergely and Szalai, Klára and Füzesi, Krisztián and Gyöngy, Miklós},
	doi = {10.1121/2.0000701},
	journal-iso = {POMA},
	journal = {PROCEEDINGS OF MEETINGS ON ACOUSTICS},
	volume = {32},
	unique-id = {30395908},
	issn = {1939-800X},
	year = {2018},
	eissn = {1939-800X}
}

@article{MTMT:30374650,
	title = {A Tensor Factorization Method for 3D Super-Resolution with Application to Dental CT},
	url = {https://m2.mtmt.hu/api/publication/30374650},
	author = {Hatvani, Janka and Basarab, Adrian and Tourneret, Jean-Yves and Gyöngy, Miklós and Kouame, Denis},
	doi = {10.1109/TMI.2018.2883517},
	journal-iso = {IEEE T MED IMAGING},
	journal = {IEEE TRANSACTIONS ON MEDICAL IMAGING},
	volume = {38},
	unique-id = {30374650},
	issn = {0278-0062},
	year = {2018},
	eissn = {1558-0062},
	pages = {1524-1531}
}