{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T00:09:39Z","timestamp":1770336579133,"version":"3.49.0"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T00:00:00Z","timestamp":1765324800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T00:00:00Z","timestamp":1765324800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100002954","name":"Universit\u00e0 degli Studi di Milano - Bicocca","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100002954","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["World Wide Web"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Accurate delineation of lesions in prostate MRI is crucial for the diagnosis of prostate cancer. Manual segmentation is time-consuming, requires advanced medical expertise, and is subject to inter-operator variability. Automatic lesion segmentation therefore represents a valuable tool to support clinicians by reducing workload, minimizing observer bias, and enabling more consistent image analysis. In this work, we investigated the performance of four deep learning architectures for prostate lesion segmentation: nnU-Net, DenseUNet, SegResUNet, and U-Net. Unlike many existing studies that rely on publicly available data, we constructed a dedicated dataset to better capture real-world variability and challenges. The dataset, comprising T2-weighted (T2W), apparent diffusion coefficient (ADC), and diffusion-weighted imaging (DWI) sequences, was carefully annotated by medical experts to ensure high-quality labels. Training was performed using the full combination of these modalities. Two cohorts were considered based on lesion severity, as defined by PI-RADS (Prostate Imaging\u2013Reporting and Data System) scores: one with only PI-RADS 4\u20135 lesions (151 patients), and another including PI-RADS 3 cases, totaling 209 patients. Evaluation was conducted both on a patient-by-patient basis and in a consolidated all-patient setting. In the patient-level analysis, nnU-Net achieved the highest Dice similarity coefficient (DSC) of 0.60 when trained on PI-RADS 4\u20135 lesions, while in the all-patient analysis, DenseUNet attained a DSC of 0.57 on the same dataset. These results are within the range reported in recent prostate lesion segmentation studies, and in some cases are comparable to or exceed those obtained with substantially larger datasets.<\/jats:p>","DOI":"10.1007\/s11280-025-01392-6","type":"journal-article","created":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T12:35:09Z","timestamp":1765370109000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Exploring UNet-based models for prostate lesion segmentation from multi-sequence MRI (T2W, ADC, DWI)"],"prefix":"10.1007","volume":"29","author":[{"given":"Saman","family":"Fouladi","sequence":"first","affiliation":[]},{"given":"Fatemeh","family":"Darvizeh","sequence":"additional","affiliation":[]},{"given":"Gabriele","family":"Gianini","sequence":"additional","affiliation":[]},{"given":"Rosario","family":"Di Meo","sequence":"additional","affiliation":[]},{"given":"Luca","family":"Di Palma","sequence":"additional","affiliation":[]},{"given":"Ernesto","family":"Damiani","sequence":"additional","affiliation":[]},{"given":"Alessandro","family":"Maiocchi","sequence":"additional","affiliation":[]},{"given":"Deborah","family":"Fazzini","sequence":"additional","affiliation":[]},{"given":"Marco","family":"Al\u00ec","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,12,10]]},"reference":[{"key":"1392_CR1","unstructured":"World Cancer Research Fund International, Prostate Cancer Statistics: Available online: https:\/\/www.wcrf.org\/preventing-cancer\/cancer-statistics\/prostate-cancer-statistics\/"},{"key":"1392_CR2","unstructured":"American Cancer Society: Key Statistics for Prostate Cancer Available online: https:\/\/www.cancer.org\/cancer\/types\/prostate-cancer\/about\/key-statistics.html"},{"key":"1392_CR3","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1002\/jmri.27599","volume":"54","author":"P Khosravi","year":"2021","unstructured":"Khosravi, P., Lysandrou, M., Eljalby, M., Li, Q., Kazemi, E., Zisimopoulos, P., Sigaras, A., Brendel, M., Barnes, J., Ricketts, C., et al.: A deep learning approach to diagnostic classification of prostate cancer using Pathology-Radiology fusion. J. Magn. Reson. Imaging. 54, 462\u2013471 (2021). https:\/\/doi.org\/10.1002\/jmri.27599","journal-title":"J. Magn. Reson. Imaging"},{"key":"1392_CR4","doi-asserted-by":"publisher","first-page":"175628722210963","DOI":"10.1177\/17562872221096377","volume":"14","author":"JW Greenberg","year":"2022","unstructured":"Greenberg, J.W., Koller, C.R., Casado, C., Triche, B.L., Krane, L.S.: A narrative review of biparametric MRI (BpMRI) implementation on Screening, Detection, and the overall accuracy for prostate cancer. Ther. Adv. Urol. 14, 17562872221096376 (2022). https:\/\/doi.org\/10.1177\/17562872221096377","journal-title":"Ther. Adv. Urol."},{"key":"1392_CR5","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1002\/jmri.27283","volume":"53","author":"T Tamada","year":"2021","unstructured":"Tamada, T., Kido, A., Yamamoto, A., Takeuchi, M., Miyaji, Y., Moriya, T., Sone, T.: Comparison of biparametric and multiparametric MRI for clinically significant prostate cancer detection with PI-RADS version 2.1. J. Magn. Reson. Imaging. 53, 283\u2013291 (2021). https:\/\/doi.org\/10.1002\/jmri.27283","journal-title":"J. Magn. Reson. Imaging"},{"key":"1392_CR6","doi-asserted-by":"publisher","unstructured":"Steiger, P., Thoeny, H.C., Prostate, M.R.I.: Based on PI-RADS version 2: How we review and report. Cancer Imaging Off Publ Int. Cancer Imaging Soc. 16 (2016). https:\/\/doi.org\/10.1186\/s40644-016-0068-2","DOI":"10.1186\/s40644-016-0068-2"},{"key":"1392_CR7","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/j.eururo.2015.07.029","volume":"70","author":"M de Rooij","year":"2016","unstructured":"de Rooij, M., Hamoen, E.H.J., Witjes, J.A., Barentsz, J.O., Rovers, M.M.: Accuracy of magnetic resonance imaging for local staging of prostate cancer: A diagnostic Meta-Analysis. Eur. Urol. 70, 233\u2013245 (2016). https:\/\/doi.org\/10.1016\/j.eururo.2015.07.029","journal-title":"Eur. Urol."},{"key":"1392_CR8","doi-asserted-by":"publisher","first-page":"376","DOI":"10.21037\/tau.2017.01.06","volume":"6","author":"MC Cabarrus","year":"2017","unstructured":"Cabarrus, M.C., Westphalen, A.C.: Multiparametric magnetic resonance imaging of the Prostate-a basic tutorial. Transl Androl. Urol. 6, 376\u2013386 (2017). https:\/\/doi.org\/10.21037\/tau.2017.01.06","journal-title":"Transl Androl. Urol."},{"key":"1392_CR9","doi-asserted-by":"publisher","first-page":"109647","DOI":"10.1016\/j.ejrad.2021.109647","volume":"138","author":"R Cuocolo","year":"2021","unstructured":"Cuocolo, R., Stanzione, A., Castaldo, A., De Lucia, D.R., Imbriaco, M.: Quality control and Whole-Gland, zonal and lesion annotations for the prostatex challenge public dataset. Eur. J. Radiol. 138, 109647 (2021). https:\/\/doi.org\/10.1016\/j.ejrad.2021.109647","journal-title":"Eur. J. Radiol."},{"key":"1392_CR10","doi-asserted-by":"crossref","unstructured":"Meng, R., Zhang, X., Huang, S., Gu, Y., Liu, G., Wu, G., Wang, N., Sun, K., Shen, D.: NaMa: Neighbor-Aware Multi-Modal Adaptive Learning for Prostate Tumor Segmentation on Anisotropic MR Images. In Proceedings of the Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence; AAAI Press, (2024)","DOI":"10.1609\/aaai.v38i5.28215"},{"key":"1392_CR11","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.U.: -Net: Convolutional Networks for Biomedical Image Segmentation BT - Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015.; Navab, N., Hornegger, J., Wells, W. M., Frangi, A. F. (eds.); Springer International Publishing: Cham, 234\u2013241. (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1392_CR12","doi-asserted-by":"publisher","first-page":"107999","DOI":"10.1016\/j.compbiomed.2024.107999","volume":"170","author":"W Wang","year":"2024","unstructured":"Wang, W., Pan, B., Ai, Y., Li, G., Fu, Y., Liu, Y., ParaCM-PNet:: A CNN-Tokenized MLP combined parallel dual pyramid network for prostate and prostate cancer segmentation in MRI. Comput. Biol. Med. 170, 107999 (2024). https:\/\/doi.org\/10.1016\/j.compbiomed.2024.107999","journal-title":"Comput. Biol. Med."},{"key":"1392_CR13","doi-asserted-by":"publisher","first-page":"e221309","DOI":"10.1148\/radiol.221309","volume":"307","author":"EC Yilmaz","year":"2023","unstructured":"Yilmaz, E.C., Shih, J.H., Belue, M.J., Harmon, S.A., Phelps, T.E., Garcia, C., Hazen, L.A., Toubaji, A., Merino, M.J., Gurram, S., et al.: Prospective evaluation of PI-RADS version 2.1 for prostate cancer detection and investigation of multiparametric MRI\u2013Derived markers. Radiology. 307, e221309 (2023). https:\/\/doi.org\/10.1148\/radiol.221309","journal-title":"Radiology"},{"key":"1392_CR14","doi-asserted-by":"crossref","unstructured":"Bonaffini, P.A., De Bernardi, E., Corsi, A., Franco, P.N., Nicoletta, D., Muglia, R., Perugini, G., Roscigno, M., Occhipinti, M., Da Pozzo, L.F., et al.: Towards the Definition of Radiomic Features and Clinical Indices to Enhance the Diagnosis of Clinically Significant Cancers in PI-RADS 4 and 5 Lesions. Cancers (Basel). 15\u00a0(2023)","DOI":"10.3390\/cancers15204963"},{"key":"1392_CR15","doi-asserted-by":"publisher","first-page":"106831","DOI":"10.1016\/j.neunet.2024.106831","volume":"181","author":"W Li","year":"2025","unstructured":"Li, W., Zheng, B., Shen, Q., Shi, X., Luo, K., Yao, Y., Li, X., Lv, S., Tao, J., Wei, Q.: Adaptive window adjustment with boundary dou loss for cascade segmentation of anatomy and lesions in prostate cancer using BpMRI. Neural Netw. 181, 106831 (2025). https:\/\/doi.org\/10.1016\/j.neunet.2024.106831","journal-title":"Neural Netw."},{"key":"1392_CR16","doi-asserted-by":"publisher","first-page":"105883","DOI":"10.1016\/j.bspc.2023.105883","volume":"90","author":"C Yan","year":"2024","unstructured":"Yan, C., Liu, F., Peng, Y., Zhao, Y., He, J., Wang, R.: 3D convolutional network with edge detection for prostate gland and tumor segmentation on T2WI and ADC. Biomed. Signal. Process. Control. 90, 105883 (2024). https:\/\/doi.org\/10.1016\/j.bspc.2023.105883","journal-title":"Biomed. Signal. Process. Control"},{"key":"1392_CR17","doi-asserted-by":"crossref","unstructured":"Thipkasorn, C., Chaichulee, S., Bejrananda, T., Tubtawee, T.: Cascaded Architecture for Segmenting Prostate Cancer Lesions in Biparametric MRI. In Proceedings of the 2024 21st International Joint Conference on Computer Science and Software Engineering (JCSSE). 167\u2013173 (2024)","DOI":"10.1109\/JCSSE61278.2024.10613671"},{"key":"1392_CR18","doi-asserted-by":"publisher","first-page":"105817","DOI":"10.1016\/j.compbiomed.2022.105817","volume":"148","author":"LC Adams","year":"2022","unstructured":"Adams, L.C., Makowski, M.R., Engel, G., Rattunde, M., Busch, F., Asbach, P., Niehues, S.M., Vinayahalingam, S., van Ginneken, B., Litjens, G., et al.: Prostate158 - An Expert-Annotated 3T MRI dataset and algorithm for prostate cancer detection. Comput. Biol. Med. 148, 105817 (2022). https:\/\/doi.org\/10.1016\/j.compbiomed.2022.105817","journal-title":"Comput. Biol. Med."},{"key":"1392_CR19","doi-asserted-by":"publisher","first-page":"7275","DOI":"10.3390\/curroncol30080528","volume":"30","author":"S Hong","year":"2023","unstructured":"Hong, S., Kim, S.H., Yoo, B., Kim, J.Y.: Deep Learning Algorithm for Tumor Segmentation and Discrimination of Clinically Significant Cancer in Patients with Prostate Cancer. Curr. Oncol. 30, 7275\u20137285 (2023)","journal-title":"Curr. Oncol."},{"key":"1392_CR20","doi-asserted-by":"publisher","first-page":"7001","DOI":"10.1002\/mp.15861","volume":"49","author":"Y Liu","year":"2022","unstructured":"Liu, Y., Zhu, Y., Wang, W., Zheng, B., Qin, X., Wang, P.: Multi-Scale discriminative network for prostate cancer lesion segmentation in multiparametric MR images. Med. Phys. 49, 7001\u20137015 (2022). https:\/\/doi.org\/10.1002\/mp.15861","journal-title":"Med. Phys."},{"key":"1392_CR21","doi-asserted-by":"publisher","first-page":"793","DOI":"10.1007\/s10278-018-0160-1","volume":"32","author":"R Alkadi","year":"2019","unstructured":"Alkadi, R., Taher, F., El-Baz, A., Werghi, N.A.: Deep Learning-Based approach for the detection and localization of prostate cancer in T2 magnetic resonance images. J. Digit. Imaging. 32, 793\u2013807 (2019). https:\/\/doi.org\/10.1007\/s10278-018-0160-1","journal-title":"J. Digit. Imaging"},{"key":"1392_CR22","doi-asserted-by":"publisher","first-page":"5216","DOI":"10.1002\/mp.15687","volume":"49","author":"ZA Eidex","year":"2022","unstructured":"Eidex, Z.A., Wang, T., Lei, Y., Axente, M., Akin-Akintayo, O.O., Ojo, O.A.A., Akintayo, A.A., Roper, J., Bradley, J.D., Liu, T., et al.: MRI-Based prostate and dominant lesion segmentation using cascaded scoring convolutional neural network. Med. Phys. 49, 5216\u20135224 (2022). https:\/\/doi.org\/10.1002\/mp.15687","journal-title":"Med. Phys."},{"key":"1392_CR23","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1016\/j.adro.2020.01.005","volume":"5","author":"Z Dai","year":"2020","unstructured":"Dai, Z., Carver, E., Liu, C., Lee, J., Feldman, A., Zong, W., Pantelic, M., Elshaikh, M., Wen, N.: Segmentation of the prostatic gland and the intraprostatic lesions on multiparametic magnetic resonance imaging using mask Region-Based convolutional neural networks. Adv. Radiat. Oncol. 5, 473\u2013481 (2020). https:\/\/doi.org\/10.1016\/j.adro.2020.01.005","journal-title":"Adv. Radiat. Oncol."},{"key":"1392_CR24","unstructured":"Kohl, S., Bonekamp, D., Schlemmer, H.-P., Yaqubi, K., Hohenfellner, M., Hadaschik, B., Radtke, J.-P., Maier-Hein, K.H.: Adversarial Networks for the Detection of Aggressive Prostate Cancer. CoRR abs\/1702.0. (2017)"},{"key":"1392_CR25","doi-asserted-by":"crossref","unstructured":"Artan, Y., Haider, M.A., Langer, D.L., Yetik, I.S.: Semi-Supervised Prostate Cancer Segmentation with Multispectral MRI. In Proceedings of the 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. 648\u2013651 (2010)","DOI":"10.1109\/ISBI.2010.5490091"},{"key":"1392_CR26","doi-asserted-by":"publisher","first-page":"906","DOI":"10.1109\/TMI.2009.2012888","volume":"28","author":"X Liu","year":"2009","unstructured":"Liu, X., Langer, D.L., Haider, M.A., Yang, Y., Wernick, M.N., Yetik, I.S.: Prostate cancer segmentation with simultaneous Estimation of Markov random field parameters and class. IEEE Trans. Med. Imaging. 28, 906\u2013915 (2009). https:\/\/doi.org\/10.1109\/TMI.2009.2012888","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1392_CR27","doi-asserted-by":"publisher","first-page":"7748","DOI":"10.1002\/mp.16557","volume":"50","author":"Z Dai","year":"2023","unstructured":"Dai, Z., Jambor, I., Taimen, P., Pantelic, M., Elshaikh, M., Dabaja, A., Rogers, C., Ettala, O., Bostr\u00f6m, P.J., Aronen, H.J., et al.: Prostate Cancer Detection and Segmentation on MRI Using Non-Local Mask R-CNN with Histopathological Ground Truth. Med. Phys. 50, 7748\u20137763 (2023). https:\/\/doi.org\/10.1002\/mp.16557","journal-title":"Med. Phys."},{"key":"1392_CR28","doi-asserted-by":"publisher","first-page":"118217","DOI":"10.1109\/ACCESS.2023.3326882","volume":"11","author":"JA Alzate-Grisales","year":"2023","unstructured":"Alzate-Grisales, J.A., Mora-Rubio, A., Garc\u00eda-Garc\u00eda, F., Tabares-Soto, R., Iglesia-Vay\u00e1, M.D.: La SAM-UNETR: Clinically significant prostate cancer segmentation using transfer learning from large model. IEEE Access. 11, 118217\u2013118228 (2023). https:\/\/doi.org\/10.1109\/ACCESS.2023.3326882","journal-title":"IEEE Access."},{"key":"1392_CR29","doi-asserted-by":"crossref","unstructured":"Gavade, A.B., Nerli, R., Kanwal, N., Gavade, P.A., Pol, S.S., Rizvi, S.T.: Automated Diagnosis of Prostate Cancer Using MpMRI Images: A Deep Learning Approach for Clinical Decision Support. Computers. 12\u00a0(2023)","DOI":"10.3390\/computers12080152"},{"key":"1392_CR30","doi-asserted-by":"publisher","unstructured":"Gunashekar, D.D., Bielak, L., H\u00e4gele, L., Oerther, B., Benndorf, M., Grosu, A.-L., Brox, T., Zamboglou, C., Bock, M.: Explainable AI for CNN-Based prostate tumor segmentation in Multi-Parametric MRI correlated to whole Mount histopathology. Radiat. Oncol. 17 (2022). https:\/\/doi.org\/10.1186\/s13014-022-02035-0","DOI":"10.1186\/s13014-022-02035-0"},{"key":"1392_CR31","doi-asserted-by":"publisher","first-page":"4786","DOI":"10.21037\/qims-22-115","volume":"12","author":"SM Rezaeijo","year":"2022","unstructured":"Rezaeijo, S.M., Jafarpoor Nesheli, S., Fatan Serj, M., Tahmasebi Birgani, M.J.: Segmentation of the Prostate, its Zones, anterior fibromuscular Stroma, and urethra on the MRIs and multimodality image fusion using U-Net model. Quant. Imaging Med. Surg. 12, 4786\u20134804 (2022). https:\/\/doi.org\/10.21037\/qims-22-115","journal-title":"Quant. Imaging Med. Surg."},{"key":"1392_CR32","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A.A., Petersen, J., Maier-Hein, K.H.: NnU-Net: A Self-Configuring method for deep Learning-Based biomedical image segmentation. Nat. Methods. 18, 203\u2013211 (2021). https:\/\/doi.org\/10.1038\/s41592-020-01008-z","journal-title":"Nat. Methods"},{"key":"1392_CR33","doi-asserted-by":"publisher","first-page":"1275","DOI":"10.21037\/qims-19-1090","volume":"10","author":"S Cai","year":"2020","unstructured":"Cai, S., Tian, Y., Lui, H., Zeng, H., Wu, Y., Chen, G.: Dense-UNet: A novel multiphoton in vivo cellular image segmentation model based on a convolutional neural network. Quant. Imaging Med. Surg. 10, 1275\u20131285 (2020). https:\/\/doi.org\/10.21037\/qims-19-1090","journal-title":"Quant. Imaging Med. Surg."},{"key":"1392_CR34","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1186\/s12880-020-00529-5","volume":"21","author":"A Saood","year":"2021","unstructured":"Saood, A., Hatem, I.: COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet. BMC Med. Imaging. 21, 19 (2021). https:\/\/doi.org\/10.1186\/s12880-020-00529-5","journal-title":"BMC Med. Imaging"},{"key":"1392_CR35","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. 2016 IEEE Conf. Comput. Vis. Pattern Recognit. 770\u2013778 (2015)","DOI":"10.1109\/CVPR.2016.90"},{"key":"1392_CR36","doi-asserted-by":"crossref","unstructured":"Weng, L., Xu, Y., Xia, M., Zhang, Y., Liu, J., Xu, Y.: Water Areas Segmentation from Remote Sensing Images Using a Separable Residual SegNet Network. ISPRS Int. J. Geo-Information 9 (2020)","DOI":"10.3390\/ijgi9040256"},{"key":"1392_CR37","unstructured":"Zaridis, D.G., Mylona, E., Marias, K., Papanikolaou, N., Tachos, N.S., Fotiadis, D.I.: A New Smart-Cropping Pipeline for Prostate Segmentation Using Deep Learning Networks. ArXiv abs\/2107.0. (2021)"},{"key":"1392_CR38","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1007\/s10278-017-0037-8","volume":"31","author":"Z Yaniv","year":"2018","unstructured":"Yaniv, Z., Lowekamp, B.C., Johnson, H.J., Beare, R.: SimpleITK Image-Analysis notebooks: A collaborative environment for education and reproducible research. J. Digit. Imaging. 31, 290\u2013303 (2018). https:\/\/doi.org\/10.1007\/s10278-017-0037-8","journal-title":"J. Digit. Imaging"},{"key":"1392_CR39","doi-asserted-by":"publisher","first-page":"1310","DOI":"10.1109\/TMI.2010.2046908","volume":"29","author":"NJ Tustison","year":"2010","unstructured":"Tustison, N.J., Avants, B.B., Cook, P.A., Zheng, Y., Egan, A., Yushkevich, P.A., Gee, J.C.: N4ITK: Improved N3 bias correction. IEEE Trans. Med. Imaging. 29, 1310\u20131320 (2010). https:\/\/doi.org\/10.1109\/TMI.2010.2046908","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1392_CR40","doi-asserted-by":"publisher","first-page":"2033","DOI":"10.1016\/j.neuroimage.2010.09.025","volume":"54","author":"BB Avants","year":"2011","unstructured":"Avants, B.B., Tustison, N.J., Song, G., Cook, P.A., Klein, A., Gee, J.C.: A reproducible evaluation of ants similarity metric performance in brain image registration. Neuroimage. 54, 2033\u20132044 (2011). https:\/\/doi.org\/10.1016\/j.neuroimage.2010.09.025","journal-title":"Neuroimage"},{"key":"1392_CR41","doi-asserted-by":"crossref","unstructured":"Abraham, N., Khan, N.: A Novel Focal Tversky Loss Function with Improved Attention U-Net for Lesion Segmentation. (2023)","DOI":"10.32920\/22734398"},{"issue":"3","key":"1392_CR42","doi-asserted-by":"publisher","first-page":"186","DOI":"10.3390\/info16030186","volume":"16","author":"S Fouladi","year":"2025","unstructured":"Fouladi, S., Di Palma, L., Darvizeh, F., Fazzini, D., Maiocchi, A., Papa, S., Gianini, G., Al\u00ec, M.: Neural network models for prostate zones segmentation in magnetic resonance imaging. Information. 16(3), 186 (2025)","journal-title":"Information"},{"key":"1392_CR43","doi-asserted-by":"crossref","unstructured":"Fouladi, S., Gianini, G., Fazzini, D., Maiocchi, A., Damiani, E., Papa, S., Ali, M.: Advanced Prostate MRI Analysis: UNET-Based Models for Zonal and Lesion Segmentation. In Proceedings of the 16th International Conference on Management of Digital Ecosystems 2024 (pp. 174\u2013187). Cham: Springer Nature Switzerland","DOI":"10.1007\/978-3-031-93598-5_13"},{"key":"1392_CR44","unstructured":"Bovio, A., Barile, M., Pallotta, F., Pede, L., Maiocchi, A., Ali, M., Darvizeh, F., Fazzini, D., Lacavalla, F., Banzi, M., Gianini, G., Mio, C., Berto, F., Bondaruc, R., Damiani, E., Fouladi, S.: A Federated Learning Architecture for Prostate MRI Image Segmentation. To appear in Proceedings of the 4th Italian Conference on Big Data and Data Science, Torino, Italy, September 2025 (ITADATA2025)"}],"container-title":["World Wide Web"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-025-01392-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11280-025-01392-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-025-01392-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T11:21:28Z","timestamp":1770290488000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11280-025-01392-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,10]]},"references-count":44,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["1392"],"URL":"https:\/\/doi.org\/10.1007\/s11280-025-01392-6","relation":{},"ISSN":["1386-145X","1573-1413"],"issn-type":[{"value":"1386-145X","type":"print"},{"value":"1573-1413","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,10]]},"assertion":[{"value":"30 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 August 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 November 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 December 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Approval for this study was obtained following a comprehensive review process, ensuring that all ethical considerations and privacy concerns were adequately addressed to protect participants\u2019 rights. Approval on 20\/11\/2024 by the Ethical Committee \u201cComitato Etico Territoriale Lombardia 3\u201d, Study ID: 5105, code \u201cPI-RADSv2\u201d, title \u201cPredizione della malignit\u00e0 delle lesioni prostatiche mediante analisi AI di immagini RM multiparametriche con mezzo di contrasto\u201d.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human ethics and consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Clinical trial number"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"4"}}