TY - JOUR AU - Hajdara, Anna AU - Cakir, Ugur AU - Molnár-Érsek, Barbara AU - Silló, Pálma AU - Széky, Balázs AU - Barna, Gábor AU - Faqi, Shaaban AU - Gyöngy, Miklós AU - Kárpáti, Sarolta AU - Németh, Krisztián AU - Mayer, Balázs TI - Targeting Melanoma-Associated Fibroblasts (MAFs) with Activated γδ (Vδ2) T Cells: An In Vitro Cytotoxicity Model JF - INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES J2 - INT J MOL SCI VL - 24 PY - 2023 IS - 16 PG - 17 SN - 1661-6596 DO - 10.3390/ijms241612893 UR - https://m2.mtmt.hu/api/publication/34110644 ID - 34110644 N1 - Funding Agency and Grant Number: Hungarian National Research, Development, and Innovation Office (HNRDI [NN 114460]; Semmelweis University Dean's Award 2015; [TKP2021-NVA-15 HNRDI] Funding text: This research was funded by the Hungarian National Research, Development, and Innovation Office (HNRDI grant NN 114460 to S.K. and K.N.) and the Semmelweis University Dean's Award 2015 (to K.N. and B.M.). Biobanking was supported by the TKP2021-NVA-15 HNRDI grant to Semmelweis University. AB - 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. LA - English DB - MTMT ER - TY - CHAP AU - Hatvani, Janka AU - Michetti, J. AU - Basarab, A. AU - Gyöngy, Miklós AU - Kouame, D. ED - IEEE, null TI - Single Image Super-Resolution Of Noisy 3d Dental Ct Images Using Tucker Decomposition T2 - 18th IEEE International Symposium on Biomedical Imaging, ISBI 2021 PB - IEEE Computer Society CY - Piscataway (NJ) SN - 9781665412469 T3 - Proceedings - International Symposium on Biomedical Imaging, ISSN 1945-7928 ; 2021-April. PY - 2021 SP - 1673 EP - 1676 PG - 4 DO - 10.1109/ISBI48211.2021.9433999 UR - https://m2.mtmt.hu/api/publication/32249821 ID - 32249821 N1 - Funding Agency and Grant Number: Erasmus+ scholarship Funding text: The authors are grateful for the Erasmus+ scholarship, which allowed this collaboration between institutions. LA - English DB - MTMT ER - TY - JOUR AU - Marosán-Vilimszky, Péter AU - Szalai, Klára AU - Horváth, András AU - Csabai, Domonkos AU - Fuzesi, Krisztian AU - Csány, Gergely AU - Gyöngy, Miklós TI - Automated Skin Lesion Classification on Ultrasound Images JF - DIAGNOSTICS J2 - DIAGNOSTICS VL - 11 PY - 2021 IS - 7 PG - 23 SN - 2075-4418 DO - 10.3390/diagnostics11071207 UR - https://m2.mtmt.hu/api/publication/32125812 ID - 32125812 N1 - Funding Agency and Grant Number: Pazmany Peter Catholic University KAP programmes; Pazmany Peter Catholic University; Jedlik Innovacio Kft [GINOP-2.1.7-15-2016-02201] Funding text: This research was in part supported by Pazmany Peter Catholic University KAP programmes. The APC was funded by Pazmany Peter Catholic University. The collaboration with Jedlik Innovacio Kft under the GINOP-2.1.7-15-2016-02201 programme is gratefully acknowledged. AB - 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%. LA - English DB - MTMT ER - TY - JOUR AU - Marosán-Vilimszky, Péter AU - Szalai, Klára AU - Csabai, Domonkos AU - Csány, Gergely AU - Horváth, András AU - Gyöngy, Miklós TI - Automated seeding for ultrasound skin lesion segmentation JF - ULTRASONICS J2 - ULTRASONICS VL - 110 PY - 2021 PG - 13 SN - 0041-624X DO - 10.1016/j.ultras.2020.106268 UR - https://m2.mtmt.hu/api/publication/31810968 ID - 31810968 AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Csány, Gergely AU - Gray, Michael D. AU - Gyöngy, Miklós TI - Estimation of Acoustic Power Output from Electrical Impedance Measurements JF - Acoustics J2 - Acoustics VL - 2 PY - 2020 IS - 1 SP - 37 EP - 50 PG - 14 SN - 2624-599X DO - 10.3390/acoustics2010004 UR - https://m2.mtmt.hu/api/publication/31289186 ID - 31289186 LA - English DB - MTMT ER - TY - JOUR AU - Makra, Ákos AU - Bost, Wolfgang AU - Kalló, Imre AU - Horváth, András AU - Fournelle, Marc AU - Gyöngy, Miklós TI - Enhancement of Acoustic Microscopy Lateral Resolution: A Comparison Between Deep Learning and Two Deconvolution Methods JF - IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL J2 - IEEE T ULTRASON FERR VL - 67 PY - 2020 IS - 1 SP - 136 EP - 145 PG - 10 SN - 0885-3010 DO - 10.1109/TUFFC.2019.2940003 UR - https://m2.mtmt.hu/api/publication/30844179 ID - 30844179 LA - English DB - MTMT ER - TY - JOUR AU - Csány, Gergely AU - Szalai, Klára AU - Gyöngy, Miklós TI - A real-time data-based scan conversion method for single element ultrasound transducers JF - ULTRASONICS J2 - ULTRASONICS VL - 93 PY - 2019 SP - 26 EP - 36 PG - 11 SN - 0041-624X DO - 10.1016/j.ultras.2018.10.006 UR - https://m2.mtmt.hu/api/publication/31046652 ID - 31046652 AB - 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. LA - English DB - MTMT ER - TY - CHAP AU - Hatvani, Janka AU - Basarab, A. AU - Michetti, J. AU - Gyöngy, Miklós AU - Kouame, D. ED - C.-C., Jay Kuo ED - Homer, H. Chen ED - Hsueh-Ming, Hang TI - Tensor-Factorization-Based 3d Single Image Super-Resolution with Semi-Blind Point Spread Function Estimation T2 - 2019 IEEE International Conference on Image Processing (ICIP) PB - IEEE CY - Piscataway (NJ) SN - 9781538662496 T3 - IEEE International Conference on Image Processing (ICIP), ISSN 1522-4880 PY - 2019 SP - 2871 EP - 2875 PG - 5 DO - 10.1109/ICIP.2019.8803354 UR - https://m2.mtmt.hu/api/publication/30870998 ID - 30870998 AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Csány, Gergely AU - Szalai, Klára AU - Füzesi, Krisztián AU - Gyöngy, Miklós TI - A low-cost portable ultrasound system for skin diagnosis JF - PROCEEDINGS OF MEETINGS ON ACOUSTICS J2 - POMA VL - 32 PY - 2018 IS - 1 SN - 1939-800X DO - 10.1121/2.0000701 UR - https://m2.mtmt.hu/api/publication/30395908 ID - 30395908 LA - English DB - MTMT ER - TY - JOUR AU - Hatvani, Janka AU - Basarab, Adrian AU - Tourneret, Jean-Yves AU - Gyöngy, Miklós AU - Kouame, Denis TI - A Tensor Factorization Method for 3D Super-Resolution with Application to Dental CT JF - IEEE TRANSACTIONS ON MEDICAL IMAGING J2 - IEEE T MED IMAGING VL - 38 PY - 2018 IS - 6 SP - 1524 EP - 1531 PG - 8 SN - 0278-0062 DO - 10.1109/TMI.2018.2883517 UR - https://m2.mtmt.hu/api/publication/30374650 ID - 30374650 N1 - Funding Agency and Grant Number: European Social Fund [EFOP-3.6.3-VEKOP-16-2017-00002]; Pazmany University [KAP17-19]; Thematic Trimester on Image Processing of the CIMI Labex, Toulouse, France [ANR-11-IDEX-0002-02, ANR-11-LABX-0040-CIMI]; European Union Funding text: This work was supported in part by the European Union, co-financed by the European Social Fund under Grant EFOP-3.6.3-VEKOP-16-2017-00002, in part by Pazmany University under Grant KAP17-19, and in part by the Thematic Trimester on Image Processing of the CIMI Labex, Toulouse, France, through the Program ANR-11-IDEX-0002-02 under Grant ANR-11-LABX-0040-CIMI. LA - English DB - MTMT ER -