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To address these challenges, the Vision\u2013Text Multimodal Feature Learning V Network (VT-MFLV) is proposed. This model exploits the complementarity between medical images and text to enhance multimodal fusion, which consequently improves critical lesion recognition accuracy. VT-MFLV introduces three key modules: Diagnostic Image\u2013Text Residual Multi-Head Semantic Encoding (DIT-RMHSE) module that preserves critical semantic cues while reducing preprocessing complexity; Fine-Grained Multimodal Fusion Local Attention Encoding (FG-MFLA) module that strengthens local cross-modal interaction; and Adaptive Global Feature Compression and Focusing (AGCF) module that emphasizes clinically relevant lesion regions. Experiments are conducted on two publicly available pulmonary infection datasets. On the MosMedData dataset, VT-MFLV achieved Dice and mIoU scores of 75.61 \u00b1 0.32% and 63.98 \u00b1 0.29%. On the QaTa-COV1 dataset, VT-MFLV achieved Dice and mIoU scores of 83.34 \u00b1 0.36% and 72.09 \u00b1 0.30%, both reaching world-leading levels.<\/jats:p>","DOI":"10.3390\/jimaging11120425","type":"journal-article","created":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T15:02:48Z","timestamp":1764774168000},"page":"425","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["VT-MFLV: Vision\u2013Text Multimodal Feature Learning V Network for Medical Image Segmentation"],"prefix":"10.3390","volume":"11","author":[{"given":"Wenju","family":"Wang","sequence":"first","affiliation":[{"name":"College of Publishing, University of Shanghai for Science and Technology, Shanghai 200093, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9407-5241","authenticated-orcid":false,"given":"Jiaqi","family":"Li","sequence":"additional","affiliation":[{"name":"College of Publishing, University of Shanghai for Science and Technology, Shanghai 200093, China"}]},{"given":"Zinuo","family":"Ye","sequence":"additional","affiliation":[{"name":"College of Publishing, University of Shanghai for Science and Technology, Shanghai 200093, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-0331-3536","authenticated-orcid":false,"given":"Yuyang","family":"Cai","sequence":"additional","affiliation":[{"name":"College of Publishing, University of Shanghai for Science and Technology, Shanghai 200093, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6480-9945","authenticated-orcid":false,"given":"Zhen","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Publishing, University of Shanghai for Science and Technology, Shanghai 200093, China"}]},{"given":"Renwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Publishing, University of Shanghai for Science and Technology, Shanghai 200093, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Boodi, D., Sudheer, N., Bidargaddi, A.P., Shatagar, S., and Telkar, M. (2024, January 24\u201326). Semantic Segmentation of Computed Tomography Scan of Lungs. Proceedings of the 5th IEEE International Conference for Emerging Technology, INCET 2024, Belgaum, India.","DOI":"10.1109\/INCET61516.2024.10593534"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"119039","DOI":"10.1109\/ACCESS.2024.3447697","article-title":"Pulmonary Nodule Segmentation Using Deep Learning: A Review","volume":"12","author":"Wang","year":"2024","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Jiang, J., Rangnekar, A., and Veeraraghavan, H. (2024). Self-supervised learning improves robustness of deep learning lung tumor segmentation to CT imaging differences. arXiv.","DOI":"10.1002\/mp.17541"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"24101","DOI":"10.1007\/s11042-023-16419-1","article-title":"A systematic literature review on deep learning approaches for pneumonia detection using chest X-ray images","volume":"83","author":"Sharma","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Tang, Y., Zhan, S., Guo, L., Pu, H., Feng, W., and Liao, J. (2022, January 23\u201325). Pulmonary embolism image segmentation based on an U\u2011net method with CBAM attention mechanism. Proceedings of the 3rd International Conference on Electronics, Communications and Information Technology, CECIT 2022, Sanya, China.","DOI":"10.1109\/CECIT58139.2022.00065"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wang, J., Chen, J., Pan, D., Chang, J., and Bi, Y. (2023, January 28\u201330). Advanced UNet++ Architecture for Precise Segmentation of COVID-19 Pulmonary Infections. Proceedings of the 2023 5th International Conference on Artificial Intelligence and Computer Applications, ICAICA 2023, Dalian, China.","DOI":"10.1109\/ICAICA58456.2023.10405457"},{"key":"ref_7","unstructured":"Auvy, A.A.M., Zannah, R., Sharif, S., Al Mahmud, W., and Noor, J. (2024, January 2\u20134). Semantic Segmentation with Attention Dense U-Net for Lung Extraction from X-ray Images. Proceedings of the 6th International Conference on Electrical Engineering and Information and Communication Technology, ICEEICT 2024, Dhaka, Bangladesh."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"052402","DOI":"10.1117\/1.JMI.9.5.052402","article-title":"Efficient multiscale fully convolutional UNet model for segmentation of 3D lung nodule from CT image","volume":"9","author":"Agnes","year":"2022","journal-title":"J. Med. Imaging"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Liu, W., Qi, Y., Li, J., and Ren, Z. (2023, January 24\u201326). Lung Nodule Segmentation Based on Complementary Context-Aware Networks. Proceedings of the 42nd Chinese Control Conference, CCC 2023, Tianjin, China.","DOI":"10.23919\/CCC58697.2023.10240177"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Pal, O.K., Roy, S., Modok, A.K., Teethi, T.I., and Sarker, S.K. (2024, January 6\u20137). ULung: A Novel Approach for Lung Image Segmentation. Proceedings of the 6th International Conference on Computing and Informatics, ICCI 2024, Cairo, Egypt.","DOI":"10.1109\/ICCI61671.2024.10485043"},{"key":"ref_11","unstructured":"Delfan, N., Moghaddam, H.A., Modaresi, M., Afshari, K., Nezamabadi, K., Pak, N., Ghaemi, O., and Forouzanfar, M. (2022). CT-LungNet: A Deep Learning Framework for Precise Lung Tissue Segmentation in 3D Thoracic CT Scans. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Li, J., Chen, Y., Wu, C., Zhang, Q., Sun, L., Patel, M., Xu, H., Lee, J., Kumar, S., and Brown, T. (2023, January 19\u201323). Pulmonary CT Nodules Segmentation Using An Enhanced Square U-Net with Depthwise Separable Convolution. Proceedings of the Medical Imaging 2023: Image Processing, San Diego, CA, USA.","DOI":"10.1117\/12.2654272"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhang, J., Tang, J., and Huo, Y. (2022, January 25\u201327). Semantic segmentation of pulmonary nodules based on attention mechanism and improved 3D U-Net. Proceedings of the 4th International Conference on Advanced Information Science and System, AISS 2022, Sanya, China.","DOI":"10.1145\/3573834.3574466"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Liu, F., Chen, Z., and Sun, P. (2022, January 18\u201320). Detection and segmentation of pulmonary nodules based on improved 3D VNet algorithm. Proceedings of the 2022 International Conference on Algorithms, Microchips and Network Applications, Zhuhai, China.","DOI":"10.1117\/12.2636396"},{"key":"ref_15","first-page":"503","article-title":"A design of interactive review for computer aided diagnosis of pulmonary nodules based on active learning","volume":"41","author":"Tan","year":"2024","journal-title":"Shengwu Yixue Gongchengxue Zazhi\/J. Biomed. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Youssef, B., Alksas, A., Shalaby, A., Mahmoud, A., Van Bogaert, E., Contractor, S., Ghazal, M., Elmaghraby, A., and El-Baz, A. (2023, January 18\u201321). A Novel Technique of Pulmonary Nodules Auto Segmentation Using Modified Convolutional Neural Networks. Proceedings of the 20th IEEE International Symposium on Biomedical Imaging (ISBI 2023), Cartagena de Indias, Colombia.","DOI":"10.1109\/ISBI53787.2023.10230705"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Jalali, Y., Fateh, M., Rezvani, M., Abolghasemi, V., and Anisi, M.H. (2021). ResBCDU-net: A deep learning framework for lung CT image segmentation. Sensors, 21.","DOI":"10.3390\/s21010268"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1007\/s13246-022-01157-9","article-title":"Pulmonary nodule segmentation based on REMU-Net","volume":"45","author":"Li","year":"2022","journal-title":"Phys. Eng. Sci. Med."},{"key":"ref_19","unstructured":"Xue, X., Wang, G., Ma, L., Jia, Q., and Wang, Y. (2022). Adjacent Slice Feature Guided 2.5d Network for Pulmonary Nodule Segmentation. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ramkumar, M.O., Jayakumar, D., and Yogesh, R. (2020, January 23\u201324). Multi Res U-Net Based Image Segmentation of Pulmonary Tuberculosis Using CT Images. Proceedings of the 7th International Conference on Smart Structures and Systems, ICSSS 2020, Chennai, India.","DOI":"10.1109\/ICSSS49621.2020.9202371"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Luo, D., He, Q., Ma, M., Yan, K., Liu, D., and Wang, P. (2023, January 18\u201323). ECANodule: Accurate Pulmonary Nodule Detection and Segmentation with Efficient Channel Attention. Proceedings of the 2023 International Joint Conference on Neural Networks, IJCNN 2023, Business Events Australia; Destination Goldcoast, Queensland, Australia.","DOI":"10.1109\/IJCNN54540.2023.10191210"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Qiu, J., Li, B., Liao, R., Mo, H., and Tian, L. (2023, January 4\u20136). A Contour-Constraint Neural Network with Hierarchical Feature Learning for Lung Nodule Segmentation in 3D CT Images. Proceedings of the 4th International Conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2023, Guangzhou, China.","DOI":"10.1109\/ICHCI58871.2023.10277887"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2081","DOI":"10.18280\/ts.410436","article-title":"An Improved Deep Network Model to Isolate Lung Nodules from Histopathological Images Using an Orchestrated and Shifted Window Vision Transformer","volume":"41","author":"Sabitha","year":"2024","journal-title":"Trait. Du Signal"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Misra, A., Rani, G., and Dhaka, V.S. (February, January 31). LSEG: Lung Segmentation for Pulmonary Disease Affected Chest Radiographs. Proceedings of the Joint 9th International Conference on Digital Arts, Media and Technology with 7th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2024, Chiang Mai, Thailand.","DOI":"10.1109\/ECTIDAMTNCON60518.2024.10479994"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1109\/TRPMS.2023.3236719","article-title":"Pulmonary Nodule Segmentation Framework Based on Fine-Tuned and Pretrained Deep Neural Network Using CT Images","volume":"7","author":"Bhattacharjee","year":"2023","journal-title":"IEEE Trans. Radiat. Plasma Med. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wei, R., Shao, J., Pu, R., Zhang, X., and Hu, C. (2020, January 6\u20138). Lesion segmentation method based on deep learning CT image of pulmonary tuberculosis. Proceedings of the 4th Annual International Conference on Data Science and Business Analytics, ICDSBA 2020, Changsha, China.","DOI":"10.1109\/ICDSBA51020.2020.00089"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yang, Q., and Chen, J.U.N. (2024). An Intelligent Model for Benign and Malignant Pulmonary Nodule Analysis Using U-Net Networks And Multilevel Attention Mechanisms. J. Mech. Med. Biol., 24.","DOI":"10.1142\/S0219519424400323"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Talib, L.F., Amin, J., Sharif, M., and Raza, M. (2024). Transformer-based semantic segmentation and CNN network for detection of histopathological lung cancer. Biomed. Signal Process. Control., 92.","DOI":"10.1016\/j.bspc.2024.106106"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Xiao, F., Shen, C., Chen, Y., Yang, T., Chen, S., Liao, Z., and Tang, J. (2021, January 9\u201312). RCGA-Net: An Improved Multi-hybrid Attention Mechanism Network in Biomedical Image Segmentation. Proceedings of the 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021, Houston, TX, USA.","DOI":"10.1109\/BIBM52615.2021.9669413"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"107810","DOI":"10.1016\/j.asoc.2021.107810","article-title":"A soft computing automatic based in deep learning with use of fine-tuning for pulmonary segmentation in computed tomography images","volume":"112","author":"Xu","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1007\/s11517-023-02957-1","article-title":"DBPNDNet: Dual-branch networks using 3DCNN toward pulmonary nodule detection","volume":"62","author":"Jian","year":"2023","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Sui, G., Liu, X., Chen, S., Liu, S., and Zhang, Z. (2023). Pulmonary nodules segmentation based on domain adaptation. Phys. Med. Biol., 68.","DOI":"10.1088\/1361-6560\/ace498"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"103909","DOI":"10.1016\/j.jvcir.2023.103909","article-title":"A dual-task region-boundary aware neural network for accurate pulmonary nodule segmentation","volume":"96","author":"Qiu","year":"2023","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"44400","DOI":"10.1109\/ACCESS.2020.2976432","article-title":"Mask R-CNN-Based Detection and Segmentation for Pulmonary Nodule 3D Visualization Diagnosis","volume":"8","author":"Cai","year":"2020","journal-title":"IEEE Access"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhu, Y., Xin, Y., Zhang, Y., Yang, D., and Xu, T. (2023). MESTrans: Multi-scale embedding spatial transformer for medical image segmentation. Comput. Methods Programs Biomed., 233.","DOI":"10.1016\/j.cmpb.2023.107493"},{"key":"ref_36","first-page":"7124902","article-title":"A Novel Deep Learning Network and Its Application for Pulmonary Nodule Segmentation","volume":"2022","author":"Lu","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Kim, Y.-G., Kim, K., Wu, D., Ren, H., Tak, W.Y., Park, S.Y., Lee, Y.R., Kang, M.K., Gil Park, J., and Kim, B.S. (2022). Deep Learning-Based Four-Region Lung Segmentation in Chest Radiography for COVID-19 Diagnosis (Research Square, 2021). Diagnostics, 12.","DOI":"10.21203\/rs.3.rs-144839\/v1"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Imran, A.-A.-Z., and Terzopoulos, D. (2021, January 10\u201315). Progressive adversarial semantic segmentation. Proceedings of the 25th International Conference on Pattern Recognition, ICPR 2020, Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9412530"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Xing, W., Zhu, Z., Hou, D., Yue, Y., Dai, F., Li, Y., Tong, L., Song, Y., and Ta, D. (2022). CM-SegNet: A deep learning-based automatic segmentation approach for medical images by combining convolution and multilayer perceptron. Comput. Biol. Med., 147.","DOI":"10.1016\/j.compbiomed.2022.105797"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Jia, J., Zhai, Z., Bakker, M.E., Hern\u00e1ndez Gir\u00f3n, I., Staring, M., and Stoel, B.C. (2021, January 13\u201316). Multi-Task Semi-Supervised Learning for Pulmonary Lobe Segmentation. Proceedings of the 18th IEEE International Symposium on Biomedical Imaging, ISBI 2021, Nice, France.","DOI":"10.1109\/ISBI48211.2021.9433985"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1109\/TMI.2023.3291719","article-title":"LViT: Language Meets Vision Transformer in Medical Image Segmentation","volume":"43","author":"Li","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_42","unstructured":"Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Morozov, S.P., Andreychenko, A.E., Pavlov, N.A., Vladzymyrskyy, A.V., Ledikhova, N.V., Gombolevskiy, V.A., Blokhin, I.A., Gelezhe, P.B., Gonchar, A.V., and Chernina, V.Y. (2020). MosMedData: Chest CT scans with COVID-19 related findings dataset. arXiv.","DOI":"10.1101\/2020.05.20.20100362"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Degerli, A., Kiranyaz, S., Chowdhury, M.E., and Gabbouj, M. (2022). Osegnet: Operational segmentation network for covid-19 detection using chest x-ray images. arXiv.","DOI":"10.1109\/ICIP46576.2022.9897412"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2021","journal-title":"Nat. Methods"},{"key":"ref_46","unstructured":"Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., and Zhou, Y. (2021). TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation. arXiv."},{"key":"ref_47","unstructured":"Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., and Li, S. (2022). TGANet: Text-Guided Attention for Improved Polyp Segmentation. Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2022 (Lecture Notes in Computer Science, Vol. 13433), Springer."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Huang, S.-C., Shen, L., Lungren, M.P., and Yeung, S. (2021, January 10\u201317). GLoRIA: A Multimodal Global-Local Representation Learning Framework for Label-Efficient Medical Image Recognition. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) 2021, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00391"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Navab, N., Hornegger, J., Wells, W., and Frangi, A. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer\u2013Assisted Intervention\u2013MICCAI 2015 (Lecture Notes in Computer Science, Vol. 9351), Springer.","DOI":"10.1007\/978-3-319-24571-3"},{"key":"ref_50","unstructured":"Oktay, O., Schlemper, J., Le Folgoc, L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., and Kainz, B. (2018, January 4\u20136). Attention U-Net: Learning Where to Look for the Pancreas. Proceedings of the 1st Conference on Medical Imaging with Deep Learning (MIDL 2018), Amsterdam, The Netherlands."},{"key":"ref_51","unstructured":"Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., and Wang, M. (2021). Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation. arXiv."},{"key":"ref_52","first-page":"2441","article-title":"UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-Wise Perspective with Transformer","volume":"36","author":"Wang","year":"2022","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_53","unstructured":"Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., and Clark, J. (2021, January 18\u201324). Learning Transferable Visual Models from Natural Language Supervision. Proceedings of the 38th International Conference on Machine Learning (ICML 2021)\/PMLR, Vienna, Austria."},{"key":"ref_54","first-page":"89","article-title":"Robust Image Watermarking Based on Hybrid IWT-DCT-SVD","volume":"1","author":"Wahyudi","year":"2025","journal-title":"Int. J. Adv. Comput. Inform. (IJACI)"},{"key":"ref_55","first-page":"41","article-title":"Robust Digital Image Watermarking Using DWT, Hessenberg, and SVD for Copyright Protection","volume":"2","author":"Kusuma","year":"2026","journal-title":"IJACI Int. J. Adv. Comput. Inform."},{"key":"ref_56","first-page":"62","article-title":"Tamper Localization and Content Restoration in Fragile Image Watermarking: A Review","volume":"2","author":"Amrullah","year":"2025","journal-title":"IJACI Int. J. Adv. Comput. Inform."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/12\/425\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T15:34:54Z","timestamp":1764776094000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/12\/425"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,28]]},"references-count":56,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["jimaging11120425"],"URL":"https:\/\/doi.org\/10.3390\/jimaging11120425","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,28]]}}}