{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T03:52:09Z","timestamp":1774410729135,"version":"3.50.1"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T00:00:00Z","timestamp":1725580800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T00:00:00Z","timestamp":1725580800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"DOI":"10.1186\/s12880-024-01385-3","type":"journal-article","created":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T12:02:36Z","timestamp":1725624156000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["nnU-Net based segmentation and 3D reconstruction of uterine fibroids with MRI images for HIFU surgery planning"],"prefix":"10.1186","volume":"24","author":[{"given":"Ting","family":"Wang","sequence":"first","affiliation":[]},{"given":"Yingang","family":"Wen","sequence":"additional","affiliation":[]},{"given":"Zhibiao","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,6]]},"reference":[{"key":"1385_CR1","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.ejogrb.2018.05.026","volume":"226","author":"J Monleon","year":"2018","unstructured":"Monleon J, et al. Epidemiology of uterine myomas and clinical practice in Spain: an observational study. Eur J Obstet Gynecol Reprod Biol. 2018;226:59\u201365.","journal-title":"Eur J Obstet Gynecol Reprod Biol"},{"issue":"4","key":"1385_CR2","doi-asserted-by":"publisher","first-page":"571","DOI":"10.1016\/j.bpobgyn.2008.04.002","volume":"22","author":"S Okolo","year":"2008","unstructured":"Okolo S. Incidence, aetiology and epidemiology of uterine fibroids. Best Pract Res Clin Obstet Gynaecol. 2008;22(4):571\u201388.","journal-title":"Best Pract Res Clin Obstet Gynaecol"},{"issue":"1","key":"1385_CR3","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.maturitas.2014.06.002","volume":"79","author":"FR Perez-Lopez","year":"2014","unstructured":"Perez-Lopez FR, et al. EMAS position statement: management of uterine fibroids. Maturitas. 2014;79(1):106\u201316.","journal-title":"Maturitas"},{"issue":"1","key":"1385_CR4","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1016\/S0002-9378(16)34501-X","volume":"99","author":"JM Brown","year":"1967","unstructured":"Brown JM, Malkasian GD. Symmonds, abdominal myomectomy. Am J Obstet Gynecol. 1967;99(1):126\u20139.","journal-title":"Am J Obstet Gynecol"},{"issue":"4","key":"1385_CR5","doi-asserted-by":"publisher","first-page":"3749","DOI":"10.3892\/etm.2017.4944","volume":"14","author":"W Xing","year":"2017","unstructured":"Xing W, et al. Curative effect of laparoscopic hysterectomy for uterine fibroids and its impact on ovarian blood supply. Experimental Therapeutic Med. 2017;14(4):3749\u201353.","journal-title":"Experimental Therapeutic Med"},{"issue":"1","key":"1385_CR6","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/S1074-3804(00)80011-0","volume":"7","author":"SR Lindheim","year":"2000","unstructured":"Lindheim SR, et al. Operative hysteroscopy in the office setting. J Am Assoc Gynecol Laparosc. 2000;7(1):65\u20139.","journal-title":"J Am Assoc Gynecol Laparosc"},{"issue":"9267","key":"1385_CR7","doi-asserted-by":"publisher","first-page":"1530","DOI":"10.1016\/S0140-6736(00)04684-5","volume":"357","author":"AS Kashyap","year":"2001","unstructured":"Kashyap AS, Kashyap S. Treatment of uterine fibroids. Lancet. 2001;357(9267):1530\u20131.","journal-title":"Lancet"},{"key":"1385_CR8","doi-asserted-by":"publisher","first-page":"e00234","DOI":"10.1016\/j.susmat.2020.e00234","volume":"28","author":"VV Komarov","year":"2021","unstructured":"Komarov VV. A review of radio frequency and microwave sustainability-oriented technologies. Sustainable Mater Technol. 2021;28:e00234.","journal-title":"Sustainable Mater Technol"},{"issue":"8","key":"1385_CR9","doi-asserted-by":"publisher","first-page":"1353","DOI":"10.7863\/ultra.32.8.1353","volume":"32","author":"VY Cheung","year":"2013","unstructured":"Cheung VY. Sonographically guided high-intensity focused ultrasound for the management of uterine fibroids. J Ultrasound Med. 2013;32(8):1353\u20138.","journal-title":"J Ultrasound Med"},{"issue":"12","key":"1385_CR10","doi-asserted-by":"publisher","first-page":"3214","DOI":"10.1016\/j.ultrasmedbio.2019.08.022","volume":"45","author":"J-S Lee","year":"2019","unstructured":"Lee J-S, et al. Safety and Efficacy of Ultrasound-guided high-intensity focused Ultrasound Treatment for Uterine fibroids and adenomyosis. Ultrasound Med Biol. 2019;45(12):3214\u201321.","journal-title":"Ultrasound Med Biol"},{"key":"1385_CR11","doi-asserted-by":"crossref","unstructured":"Vasudeva Rao SK, Lingappa B. Image analysis for MRI based Brain Tumour Detection using hybrid segmentation and deep learning classification technique. Int J Intell Eng Syst, 2019. 12(5).","DOI":"10.22266\/ijies2019.1031.06"},{"issue":"12","key":"1385_CR12","doi-asserted-by":"publisher","first-page":"93","DOI":"10.31026\/j.eng.2022.12.07","volume":"28","author":"NM Ghadi","year":"2022","unstructured":"Ghadi NM, Salman NH. Deep learning-based segmentation and classification techniques for brain tumor MRI: a review. J Eng. 2022;28(12):93\u2013112.","journal-title":"J Eng"},{"key":"1385_CR13","doi-asserted-by":"publisher","first-page":"106405","DOI":"10.1016\/j.compbiomed.2022.106405","volume":"152","author":"R Ranjbarzadeh","year":"2023","unstructured":"Ranjbarzadeh R, Caputo A, Tirkolaee EB, et al. Brain tumor segmentation of MRI images: a comprehensive review on the application of artificial intelligence tools. Comput Biol Med. 2023;152:106405.","journal-title":"Comput Biol Med"},{"issue":"2","key":"1385_CR14","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee F, et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods. 2021;18(2):203\u201311.","journal-title":"Nat Methods"},{"key":"1385_CR15","doi-asserted-by":"publisher","first-page":"52726","DOI":"10.1109\/ACCESS.2023.3272987","volume":"11","author":"V Chandrasekar","year":"2023","unstructured":"Chandrasekar V, Ansari MY, Singh AV, et al. Investigating the use of machine learning models to understand the drugs permeability across placenta. IEEE Access. 2023;11:52726\u201339.","journal-title":"IEEE Access"},{"key":"1385_CR16","doi-asserted-by":"publisher","first-page":"9890","DOI":"10.1109\/ACCESS.2022.3233110","volume":"11","author":"MY Ansari","year":"2022","unstructured":"Ansari MY, Chandrasekar V, Singh AV, et al. Re-routing drugs to blood brain barrier: a comprehensive analysis of machine learning approaches with fingerprint amalgamation and data balancing. IEEE Access. 2022;11:9890\u2013906.","journal-title":"IEEE Access"},{"key":"1385_CR17","doi-asserted-by":"publisher","first-page":"105532","DOI":"10.1016\/j.engappai.2022.105532","volume":"117","author":"A Al-Kababji","year":"2023","unstructured":"Al-Kababji A, Bensaali F, Dakua SP, et al. Automated liver tissues delineation techniques: a systematic survey on machine learning current trends and future orientations. Eng Appl Artif Intell. 2023;117:105532.","journal-title":"Eng Appl Artif Intell"},{"key":"1385_CR18","doi-asserted-by":"crossref","unstructured":"Ansari MY, Qaraqe M, Charafeddine F et al. Estimating age and gender from electrocardiogram signals: a comprehensive review of the past decade. Artif Intell Med, 2023; 102690.","DOI":"10.1016\/j.artmed.2023.102690"},{"key":"1385_CR19","doi-asserted-by":"publisher","first-page":"4589","DOI":"10.1109\/ACCESS.2023.3234519","volume":"11","author":"MY Ansari","year":"2023","unstructured":"Ansari MY, Qaraqe M, Mefood. A large-scale representative benchmark of quotidian foods for the middle east. IEEE Access. 2023;11:4589\u2013601.","journal-title":"IEEE Access"},{"key":"1385_CR20","doi-asserted-by":"publisher","first-page":"24528","DOI":"10.1109\/ACCESS.2022.3154771","volume":"10","author":"S Mohanty","year":"2022","unstructured":"Mohanty S, Dakua SP. Toward computing cross-modality symmetric non-rigid medical image registration. IEEE Access. 2022;10:24528\u201339.","journal-title":"IEEE Access"},{"issue":"1","key":"1385_CR21","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1007\/s13721-023-00412-7","volume":"12","author":"Y Regaya","year":"2023","unstructured":"Regaya Y, Amira A, Dakua SP. Development of a cerebral aneurysm segmentation method to prevent sentinel hemorrhage. Netw Model Anal Health Inf Bioinf. 2023;12(1):18.","journal-title":"Netw Model Anal Health Inf Bioinf"},{"issue":"17","key":"1385_CR22","doi-asserted-by":"publisher","first-page":"e5184","DOI":"10.1002\/cpe.5184","volume":"31","author":"X Zhai","year":"2019","unstructured":"Zhai X, Amira A, Bensaali F, et al. Zynq SoC based acceleration of the lattice boltzmann method. Concurrency Computation: Pract Experience. 2019;31(17):e5184.","journal-title":"Concurrency Computation: Pract Experience"},{"key":"1385_CR23","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1007\/s11548-020-02120-3","volume":"15","author":"SS Esfahani","year":"2020","unstructured":"Esfahani SS, Zhai X, Chen M, et al. Lattice-boltzmann interactive blood flow simulation pipeline. Int J Comput Assist Radiol Surg. 2020;15:629\u201339.","journal-title":"Int J Comput Assist Radiol Surg"},{"issue":"2","key":"1385_CR24","doi-asserted-by":"publisher","first-page":"1592","DOI":"10.1109\/JSYST.2019.2952459","volume":"14","author":"X Zhai","year":"2019","unstructured":"Zhai X, Chen M, Esfahani SS, et al. Heterogeneous system-on-chip-based Lattice-Boltzmann visual simulation system. IEEE Syst J. 2019;14(2):1592\u2013601.","journal-title":"IEEE Syst J"},{"key":"1385_CR25","doi-asserted-by":"publisher","first-page":"109512","DOI":"10.1016\/j.knosys.2022.109512","volume":"253","author":"Z Han","year":"2022","unstructured":"Han Z, Jian M, Wang GG, ConvUNeXt. An efficient convolution neural network for medical image segmentation. Knowl Based Syst. 2022;253:109512.","journal-title":"Knowl Based Syst"},{"issue":"1","key":"1385_CR26","doi-asserted-by":"publisher","first-page":"14153","DOI":"10.1038\/s41598-022-16828-6","volume":"12","author":"MY Ansari","year":"2022","unstructured":"Ansari MY, Yang Y, Balakrishnan S, et al. A lightweight neural network with multiscale feature enhancement for liver CT segmentation. Sci Rep. 2022;12(1):14153.","journal-title":"Sci Rep"},{"key":"1385_CR27","doi-asserted-by":"crossref","unstructured":"Jafari M, Auer D, Francis S et al. DRU-Net: an efficient deep convolutional neural network for medical image segmentation. 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). IEEE, 2020: 1144\u20131148.","DOI":"10.1109\/ISBI45749.2020.9098391"},{"key":"1385_CR28","doi-asserted-by":"publisher","first-page":"106478","DOI":"10.1016\/j.compbiomed.2022.106478","volume":"153","author":"MY Ansari","year":"2023","unstructured":"Ansari MY, Yang Y, Meher PK, et al. Dense-PSP-UNet: a neural network for fast inference liver ultrasound segmentation. Comput Biol Med. 2023;153:106478.","journal-title":"Comput Biol Med"},{"key":"1385_CR29","doi-asserted-by":"crossref","unstructured":"Xie Y, Zhang J, Shen C et al. Cotr: Efficiently bridging cnn and transformer for 3d medical image segmentation. Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part III 24. Springer International Publishing, 2021: 171\u2013180.","DOI":"10.1007\/978-3-030-87199-4_16"},{"issue":"1","key":"1385_CR30","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1186\/s12880-022-00825-2","volume":"22","author":"MY Ansari","year":"2022","unstructured":"Ansari MY, Abdalla A, Ansari MY, et al. Practical utility of liver segmentation methods in clinical surgeries and interventions. BMC Med Imaging. 2022;22(1):97.","journal-title":"BMC Med Imaging"},{"issue":"6","key":"1385_CR31","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1109\/TRPMS.2021.3071148","volume":"6","author":"Y Akhtar","year":"2021","unstructured":"Akhtar Y, Dakua SP, Abdalla A, et al. Risk assessment of computer-aided diagnostic software for hepatic resection. IEEE Trans Radiation Plasma Med Sci. 2021;6(6):667\u201377.","journal-title":"IEEE Trans Radiation Plasma Med Sci"},{"issue":"13","key":"1385_CR32","doi-asserted-by":"publisher","first-page":"14225","DOI":"10.1002\/cam4.6089","volume":"12","author":"P Rai","year":"2023","unstructured":"Rai P, Ansari MY, Warfa M, et al. Efficacy of fusion imaging for immediate post-ablation assessment of malignant liver neoplasms: a systematic review. Cancer Med. 2023;12(13):14225\u201351.","journal-title":"Cancer Med"},{"key":"1385_CR33","doi-asserted-by":"crossref","unstructured":"Ansari MY, Mangalote IAC, Meher PK, et al. Advancements in Deep Learning for B-Mode Ultrasound Segmentation: a Comprehensive Review. IEEE Transactions on Emerging Topics in Computational Intelligence; 2024.","DOI":"10.1109\/TETCI.2024.3377676"},{"issue":"11","key":"1385_CR34","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun Y, et al. Gradient-based learning applied to document recognition. Proc IEEE. 1998;86(11):2278\u2013324.","journal-title":"Proc IEEE"},{"key":"1385_CR35","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.media.2019.01.012","volume":"53","author":"J Schlemper","year":"2019","unstructured":"Schlemper J, et al. Attention gated networks: learning to leverage salient regions in medical images. Med Image Anal. 2019;53:197\u2013207.","journal-title":"Med Image Anal"},{"key":"1385_CR36","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1016\/j.media.2017.08.006","volume":"42","author":"X Yang","year":"2017","unstructured":"Yang X, et al. Co-trained convolutional neural networks for automated detection of prostate cancer in multi-parametric MRI. Med Image Anal. 2017;42:212\u201327.","journal-title":"Med Image Anal"},{"issue":"5","key":"1385_CR37","doi-asserted-by":"publisher","first-page":"1229","DOI":"10.1109\/TMI.2016.2528821","volume":"35","author":"T Brosch","year":"2016","unstructured":"Brosch T, et al. Deep 3D Convolutional Encoder Networks with shortcuts for Multiscale feature Integration Applied to multiple sclerosis lesion segmentation. IEEE Trans Med Imaging. 2016;35(5):1229\u201339.","journal-title":"IEEE Trans Med Imaging"},{"key":"1385_CR38","volume-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation. In Medical Image Computing and Computer-assisted intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation. In Medical Image Computing and Computer-assisted intervention \u2013 MICCAI 2015. Cham: Springer International Publishing; 2015."},{"issue":"4","key":"1385_CR39","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","volume":"39","author":"E Shelhamer","year":"2017","unstructured":"Shelhamer E, Long J, Darrell T. Fully Convolutional Networks for Semantic Segmentation. IEEE Trans Pattern Anal Mach Intell. 2017;39(4):640\u201351.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1385_CR40","doi-asserted-by":"publisher","first-page":"105395","DOI":"10.1016\/j.cmpb.2020.105395","volume":"192","author":"Z Zhang","year":"2020","unstructured":"Zhang Z, et al. DENSE-INception U-net for medical image segmentation. Comput Methods Programs Biomed. 2020;192:105395.","journal-title":"Comput Methods Programs Biomed"},{"key":"1385_CR41","first-page":"1","volume":"2017","author":"AH Curiale","year":"2017","unstructured":"Curiale AH, et al. Automatic myocardial segmentation by using a deep learning network in cardiac MRI. XLIII Latin Am Comput Conf (CLEI). 2017;2017:1\u20136.","journal-title":"XLIII Latin Am Comput Conf (CLEI)"},{"key":"1385_CR42","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.neunet.2019.08.025","volume":"121","author":"N Ibtehaz","year":"2020","unstructured":"Ibtehaz N, Rahman MS. MultiResUNet: rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Netw. 2020;121:74\u201387.","journal-title":"Neural Netw"},{"key":"1385_CR43","doi-asserted-by":"crossref","unstructured":"Alom MZ et al. Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation. ArXiv, 2018. abs\/1802.06955.","DOI":"10.1109\/NAECON.2018.8556686"},{"key":"1385_CR44","doi-asserted-by":"crossref","unstructured":"Yu L et al. Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets. in MICCAI. 2017.","DOI":"10.1007\/978-3-319-66185-8_33"},{"key":"1385_CR45","doi-asserted-by":"crossref","unstructured":"Ben-Zadok N, Riklin-Raviv T, Kiryati N. Interactive level set segmentation for image-guided therapy. in 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. 2009. IEEE.","DOI":"10.1109\/ISBI.2009.5193243"},{"issue":"02","key":"1385_CR46","first-page":"1450030","volume":"26","author":"H Khotanlou","year":"2014","unstructured":"Khotanlou H, et al. Segmentation of uterine fibroid on mr images based on Chan\u2013Vese level set method and shape prior model. Biomedical Engineering: Appl Basis Commun. 2014;26(02):1450030.","journal-title":"Biomedical Engineering: Appl Basis Commun"},{"key":"1385_CR47","doi-asserted-by":"crossref","unstructured":"Yao J, et al. Uterine fibroid segmentation and volume measurement on MRI. In Medical Imaging 2006: physiology, function, and structure from medical images. SPIE; 2006.","DOI":"10.1117\/12.653856"},{"issue":"3","key":"1385_CR48","doi-asserted-by":"publisher","first-page":"150","DOI":"10.5812\/kmp.iranjradiol.17351065.3142","volume":"8","author":"A Fallahi","year":"2011","unstructured":"Fallahi A, et al. Uterine segmentation and volume measurement in uterine fibroid patients\u2019 MRI using fuzzy C-mean algorithm and morphological operations. Iran J Radiol. 2011;8(3):150.","journal-title":"Iran J Radiol"},{"key":"1385_CR49","doi-asserted-by":"crossref","unstructured":"Fallahi A et al. Uterine fibroid segmentation on multiplan MRI using FCM, MPFCM and morphological operations. in. 2010 2nd International Conference on Computer Engineering and Technology. 2010. IEEE.","DOI":"10.1109\/ICCET.2010.5485920"},{"key":"1385_CR50","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1016\/j.compbiomed.2015.04.030","volume":"62","author":"C Militello","year":"2015","unstructured":"Militello C, et al. A fully automatic 2D segmentation method for uterine fibroid in MRgFUS treatment evaluation. Comput Biol Med. 2015;62:277\u201392.","journal-title":"Comput Biol Med"},{"key":"1385_CR51","doi-asserted-by":"crossref","unstructured":"Militello C et al. A semi-automatic multi-seed region-growing approach for uterine fibroids segmentation in MRgFUS treatment. in 2013 Seventh International Conference on Complex, Intelligent, and Software Intensive Systems. 2013. IEEE.","DOI":"10.1109\/CISIS.2013.36"},{"key":"1385_CR52","doi-asserted-by":"publisher","first-page":"1071","DOI":"10.1007\/s11517-015-1404-6","volume":"54","author":"L Rundo","year":"2016","unstructured":"Rundo L, et al. Combining split-and-merge and multi-seed region growing algorithms for uterine fibroid segmentation in MRgFUS treatments. Med Biol Eng Comput. 2016;54:1071\u201384.","journal-title":"Med Biol Eng Comput"},{"issue":"7","key":"1385_CR53","doi-asserted-by":"publisher","first-page":"073502","DOI":"10.1118\/1.4881319","volume":"41","author":"K Antila","year":"2014","unstructured":"Antila K, et al. Automatic segmentation for detecting uterine fibroid regions treated with MR-guided high intensity focused ultrasound (MR\u2010HIFU). Med Phys. 2014;41(7):073502.","journal-title":"Med Phys"},{"key":"1385_CR54","volume-title":"VETOT, volume estimation and Tracking over Time: Framework and Validation. In Medical Image Computing and Computer-assisted intervention - MICCAI 2003","author":"J-P Guyon","year":"2003","unstructured":"Guyon J-P, et al. VETOT, volume estimation and Tracking over Time: Framework and Validation. In Medical Image Computing and Computer-assisted intervention - MICCAI 2003. Berlin, Heidelberg: Springer Berlin Heidelberg; 2003."},{"issue":"3","key":"1385_CR55","first-page":"20","volume":"3","author":"A Sasidharan","year":"2010","unstructured":"Sasidharan A, Malarkhodi S. Segmentation and volume measurement of uterine fibroid on MRI images. Int J Adv Eng Appl. 2010;3(3):20\u20136.","journal-title":"Int J Adv Eng Appl"},{"key":"1385_CR56","doi-asserted-by":"publisher","first-page":"103438","DOI":"10.1016\/j.compbiomed.2019.103438","volume":"114","author":"Y Kurata","year":"2019","unstructured":"Kurata Y, et al. Automatic segmentation of the uterus on MRI using a convolutional neural network. Comput Biol Med. 2019;114:103438.","journal-title":"Comput Biol Med"},{"issue":"1","key":"1385_CR57","first-page":"1","volume":"71","author":"C TANG","year":"2020","unstructured":"TANG C, YU X. MRI image segmentation system of uterine fibroids based on AR-Unet network. Am Sci Res J Eng Technol Sci (ASRJETS). 2020;71(1):1\u201310.","journal-title":"Am Sci Res J Eng Technol Sci (ASRJETS)"},{"issue":"11","key":"1385_CR58","doi-asserted-by":"publisher","first-page":"3309","DOI":"10.1109\/TMI.2020.2991266","volume":"39","author":"C Zhang","year":"2020","unstructured":"Zhang C, et al. HIFUNet: Multi-class Segmentation of uterine regions from MR images using global Convolutional networks for HIFU surgery planning. IEEE Trans Med Imaging. 2020;39(11):3309\u201320.","journal-title":"IEEE Trans Med Imaging"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-024-01385-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-024-01385-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-024-01385-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T13:04:46Z","timestamp":1725627886000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-024-01385-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,6]]},"references-count":58,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["1385"],"URL":"https:\/\/doi.org\/10.1186\/s12880-024-01385-3","relation":{},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,6]]},"assertion":[{"value":"18 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 August 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 September 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"In studies involving human participants, all procedures complied with the ethical standards of the institutional and national research committees, as well as the 1964 Declaration of Helsinki and its later amendments or similar ethical standards. The study was approved by the institutional review board of Chongqing Haifu Hospital. Informed consent was waived by the Institutional Review Board of Chongqing Haifu Hospital, given the nature of this retrospective study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"233"}}