{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T14:57:52Z","timestamp":1781708272811,"version":"3.54.5"},"reference-count":45,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,7,14]],"date-time":"2021-07-14T00:00:00Z","timestamp":1626220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Program ERDF of the Valencian Community 2014-2020 ,European Union","award":["IDIFEDER\/2020\/030"],"award-info":[{"award-number":["IDIFEDER\/2020\/030"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Atrial fibrillation (AF) is the most common cardiac arrhythmia. At present, cardiac ablation is the main treatment procedure for AF. To guide and plan this procedure, it is essential for clinicians to obtain patient-specific 3D geometrical models of the atria. For this, there is an interest in automatic image segmentation algorithms, such as deep learning (DL) methods, as opposed to manual segmentation, an error-prone and time-consuming method. However, to optimize DL algorithms, many annotated examples are required, increasing acquisition costs. The aim of this work is to develop automatic and high-performance computational models for left and right atrium (LA and RA) segmentation from a few labelled MRI volumetric images with a 3D Dual U-Net algorithm. For this, a supervised domain adaptation (SDA) method is introduced to infer knowledge from late gadolinium enhanced (LGE) MRI volumetric training samples (80 LA annotated samples) to a network trained with balanced steady-state free precession (bSSFP) MR images of limited number of annotations (19 RA and LA annotated samples). The resulting knowledge-transferred model SDA outperformed the same network trained from scratch in both RA (Dice equals 0.9160) and LA (Dice equals 0.8813) segmentation tasks.<\/jats:p>","DOI":"10.3390\/e23070898","type":"journal-article","created":{"date-parts":[[2021,7,14]],"date-time":"2021-07-14T21:56:51Z","timestamp":1626299811000},"page":"898","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Supervised Domain Adaptation for Automated Semantic Segmentation of the Atrial Cavity"],"prefix":"10.3390","volume":"23","author":[{"given":"Marta","family":"Saiz-Viv\u00f3","sequence":"first","affiliation":[{"name":"Instituto de Investigaci\u00f3n e Innovaci\u00f3n en Bioingenier\u00eda, Universitat Polit\u00e8cnica de Val\u00e8ncia, 46022 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7616-6029","authenticated-orcid":false,"given":"Adri\u00e1n","family":"Colomer","sequence":"additional","affiliation":[{"name":"Instituto de Investigaci\u00f3n e Innovaci\u00f3n en Bioingenier\u00eda, Universitat Polit\u00e8cnica de Val\u00e8ncia, 46022 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carles","family":"Fonfr\u00eda","sequence":"additional","affiliation":[{"name":"Radiology Department, La Fe University and Polytechnic Hospital, 46026 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8234-010X","authenticated-orcid":false,"given":"Luis","family":"Mart\u00ed-Bonmat\u00ed","sequence":"additional","affiliation":[{"name":"Radiology Department, La Fe University and Polytechnic Hospital, 46026 Valencia, Spain"},{"name":"Biomedical Imaging Research Group (GIBI230-PREBI), La Fe Health Research Institute, 46026 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Valery","family":"Naranjo","sequence":"additional","affiliation":[{"name":"Instituto de Investigaci\u00f3n e Innovaci\u00f3n en Bioingenier\u00eda, Universitat Polit\u00e8cnica de Val\u00e8ncia, 46022 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1177\/1747493019897870","article-title":"Global epidemiology of atrial fibrillation: An increasing epidemic and public health challenge","volume":"16","author":"Lippi","year":"2021","journal-title":"Int. J. Stroke"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"101595","DOI":"10.1016\/j.media.2019.101595","article-title":"Atrial scar quantification via multi-scale CNN in the graph-cuts framework","volume":"60","author":"Li","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref_3","unstructured":"Zipes, D.P., and Jalife, J. (2004). Cardiac Electrophysiology: From Cell to Bedside: Seventh Edition, W.B. Saunders. [4th ed.]."},{"key":"ref_4","first-page":"57","article-title":"Electroanatomical mapping of atrial fibrillation: Review of the current techniques and advances","volume":"7","author":"Rolf","year":"2014","journal-title":"J. Atr. Fibrillation"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.jcct.2019.03.005","article-title":"Multimodality imaging of left atrium in patients with atrial fibrillation","volume":"13","author":"Guglielmo","year":"2019","journal-title":"J. Cardiovasc. Comput. Tomogr."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1161\/CIRCEP.113.000689","article-title":"Atrial fibrillation ablation outcome is predicted by left atrial remodeling on MRI","volume":"7","author":"McGann","year":"2014","journal-title":"Circ. Arrhythmia Electrophysiol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2390","DOI":"10.1093\/eurheartj\/ehv233","article-title":"Atrial fibrillation driven by micro-anatomic intramural re-entry revealed by simultaneous sub-epicardial and sub-endocardial optical mapping in explanted human hearts","volume":"36","author":"Hansen","year":"2015","journal-title":"Eur. Heart J."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhao, J., Hansen, B.J., Wang, Y., Csepe, T.A., Sul, L.V., Tang, A., Yuan, Y., Li, N., Bratasz, A., and Powell, K.A. (2017). Three-dimensional integrated functional, structural, and computational mapping to define the structural \u201cfingerprints\u201d of heart-specific atrial fibrillation drivers in human heart ex vivo. J. Am. Heart Assoc., 6.","DOI":"10.1161\/JAHA.117.005922"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.compmedimag.2017.05.001","article-title":"Fully automatic segmentation of left ventricular anatomy in 3-D LGE-MRI","volume":"59","author":"Kurzendorfer","year":"2017","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Vesal, S., Ravikumar, N., and Maier, A. (2019). Automated Multi-Sequence Cardiac MRI Segmentation Using Supervised Domain Adaptation, Springer.","DOI":"10.1007\/978-3-030-39074-7_32"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Li, C., Tong, Q., Liao, X., Si, W., Sun, Y., Wang, Q., and Heng, P.A. (2019). Attention Based Hierarchical Aggregation Network for 3D Left Atrial Segmentation. International Workshop on Statistical Atlases and Computational Models of the Heart, Springer.","DOI":"10.1007\/978-3-030-12029-0_28"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Vesal, S., Ravikumar, N., and Maier, A. (2019). Dilated Convolutions in Neural Networks for Left Atrial Segmentation in 3D Gadolinium Enhanced-MRI. International Workshop on Statistical Atlases and Computational Models of the Heart, Springer.","DOI":"10.1007\/978-3-030-12029-0_35"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Vesal, S., Maier, A., and Ravikumar, N. (2020). Fully Automated 3D Cardiac MRI Localisation and Segmentation Using Deep Neural Networks. J. Imaging, 6.","DOI":"10.3390\/jimaging6070065"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Jia, S., Despinasse, A., Wang, Z., Delingette, H., Pennec, X., Ja\u00efs, P., Cochet, H., and Sermesant, M. (2019). Automatically Segmenting the Left Atrium from Cardiac Images Using Successive 3D U-Nets and a Contour Loss. Lect. Notes Comput. Sci.","DOI":"10.1007\/978-3-030-12029-0_24"},{"key":"ref_15","unstructured":"Saiz-Viv\u00f3, M., Colomer, A., and Naranjo, V. (2020, January 25\u201327). Deep convolutional encoder-decoder network for semantic segmentation of atrial cavity. Proceedings of the XXXVIII Congreso Anual de la Sociedad Espa\u00f1ola de Ingenier\u00eda Biom\u00e9dica (CASEIB 2020), Valladolid, Spain."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1016\/j.ultrasmedbio.2003.12.001","article-title":"Watershed segmentation for breast tumor in 2-D sonography","volume":"30","author":"Huang","year":"2004","journal-title":"Ultrasound Med. Biol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/j.bspc.2012.01.002","article-title":"Automatic liver segmentation in MRI images using an iterative watershed algorithm and artificial neural network","volume":"7","author":"Masoumi","year":"2012","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1797","DOI":"10.1109\/TMI.2015.2409024","article-title":"Automated vessel segmentation using infinite perimeter active contour model with hybrid region information with application to retinal images","volume":"34","author":"Zhao","year":"2015","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ciecholewski, M., and Spodnik, J.H. (2018). Semi\u2013automatic corpus callosum segmentation and 3d visualization using active contour methods. Symmetry, 10.","DOI":"10.3390\/sym10110589"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1109\/TMI.2015.2496296","article-title":"Superpixel-based segmentation for 3D prostate MR images","volume":"35","author":"Tian","year":"2015","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.media.2018.05.006","article-title":"Superpixel and multi-atlas based fusion entropic model for the segmentation of X-ray images","volume":"48","author":"Nguyen","year":"2018","journal-title":"Med. Image Anal."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Jamart, K., Xiong, Z., Maso Talou, G.D., Stiles, M.K., and Zhao, J. (2020). Mini Review: Deep Learning for Atrial Segmentation From Late Gadolinium-Enhanced MRIs. Front. Cardiovasc. Med.","DOI":"10.3389\/fcvm.2020.00086"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","article-title":"U-net: Convolutional networks for biomedical image segmentation","volume":"9351","author":"Ronneberger","year":"2015","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., and Ronneberger, O. (2016). 3D U-net: Learning dense volumetric segmentation from sparse annotation. Lect. Notes Comput. Sci., 424\u2013432.","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"ref_25","unstructured":"(2020, March 10). 2018 Atrial Segmentation Challenge. Available online: http:\/\/atriaseg2018.cardiacatlas.org\/."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Xia, Q., Yao, Y., Hu, Z., and Hao, A. (2018). Automatic 3D atrial segmentation from GE-MRIs using volumetric fully convolutional networks. International Workshop on Statistical Atlases and Computational Models of the Heart, Springer.","DOI":"10.1007\/978-3-030-12029-0_23"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yang, X., Wang, N., Wang, Y., Wang, X., Nezafat, R., Ni, D., and Heng, P.A. (2018). Combating uncertainty with novel losses for automatic left atrium segmentation. International Workshop on Statistical Atlases and Computational Models of the Heart, Springer.","DOI":"10.1007\/978-3-030-12029-0_27"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.media.2019.03.009","article-title":"Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis","volume":"54","author":"Cheplygina","year":"2019","journal-title":"Med. Image Anal."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/MSP.2014.2347059","article-title":"Visual Domain Adaptation: A survey of recent advances","volume":"32","author":"Patel","year":"2015","journal-title":"IEEE Signal Process Mag."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Motiian, S., Piccirilli, M., Adjeroh, D.A., and Doretto, G. (2017, January 22\u201329). Unified Deep Supervised Domain Adaptation and Generalization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.609"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.media.2016.02.005","article-title":"Supervised domain adaptation of decision forests: Transfer of models trained in vitro for in vivo intravascular ultrasound tissue characterization","volume":"32","author":"Conjeti","year":"2016","journal-title":"Med. Image Anal."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Hofer, C., Kwitt, R., Holler, Y., Trinka, E., and Uhl, A. (2017, January 18\u201321). Simple domain adaptation for cross-dataset analyses of brain MRI data. Proceedings of the 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, VIC, Australia.","DOI":"10.1109\/ISBI.2017.7950556"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1016\/j.neuroimage.2016.05.053","article-title":"Domain adaptation for Alzheimer\u2019s disease diagnostics","volume":"139","author":"Wachinger","year":"2016","journal-title":"Neuroimage"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Menegola, A., Fornaciali, M., Pires, R., Bittencourt, F.V., Avila, S., and Valle, E. (2017, January 18\u201321). Knowledge transfer for melanoma screening with deep learning. Proceedings of the 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, VIC, Australia.","DOI":"10.1109\/ISBI.2017.7950523"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ribeiro, E., H\u00e4fner, M., Wimmer, G., Tamaki, T., Tischendorf, J.J.W., Yoshida, S., Tanaka, S., and Uhl, A. (2017, January 18\u201321). Exploring texture Transfer Learning for Colonic Polyp Classification via Convolutional Neural Networks. Proceedings of the 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, VIC, Australia.","DOI":"10.1109\/ISBI.2017.7950695"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Kamnitsas, K., Baumgartner, C., Ledig, C., Newcombe, V., Simpson, J., Kane, A., Menon, D., Nori, A., Criminisi, A., and Rueckert, D. (2017). Unsupervised domain adaptation in brain lesion segmentation with adversarial networks. International Conference on Information Processing in Medical Imaging, Springer.","DOI":"10.1007\/978-3-319-59050-9_47"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-43299-z","article-title":"Supervised Domain Adaptation for Automatic Sub-cortical Brain Structure Segmentation with Minimal User Interaction","volume":"9","author":"Kushibar","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ghafoorian, M., Mehrtash, A., Kapur, T., Karssemeijer, N., Marchiori, E., Pesteie, M., Guttmann, C.R.G., de Leeuw, F.E., Tempany, C.M., and van Ginneken, B. (2017). Transfer learning for domain adaptation in MRI: Application in brain lesion segmentation. Lect. Notes Comput. Sci.","DOI":"10.1007\/978-3-319-66179-7_59"},{"key":"ref_40","unstructured":"(2020, March 10). MM-WHS: Multi-Modality Whole Heart Segmentation. Available online: http:\/\/www.sdspeople.fudan.edu.cn\/zhuangxiahai\/0\/mmwhs\/."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"101537","DOI":"10.1016\/j.media.2019.101537","article-title":"Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge","volume":"58","author":"Zhuang","year":"2019","journal-title":"Med. Image Anal."},{"key":"ref_42","unstructured":"Cardiac Atlas Project (2020, March 08). Left Atrium Segmentation Challenge. Available online: https:\/\/www.cardiacatlas.org\/challenges\/left-atrium-segmentation-challenge\/."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1460","DOI":"10.1109\/TMI.2015.2398818","article-title":"Benchmark for Algorithms Segmenting the Left Atrium from 3D CT and MRI Datasets","volume":"34","author":"Geers","year":"2015","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., and Maier-Hein, K.H. (2018). Brain tumor segmentation and radiomics survival prediction: Contribution to the BRATS 2017 challenge. Lect. Notes Comput. Sci.","DOI":"10.1007\/978-3-319-75238-9_25"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., and Ahmadi, S.A. (2016, January 25\u201328). V-Net: Fully convolutional neural networks for volumetric medical image segmentation. Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA.","DOI":"10.1109\/3DV.2016.79"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/23\/7\/898\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:30:41Z","timestamp":1760164241000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/23\/7\/898"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,14]]},"references-count":45,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["e23070898"],"URL":"https:\/\/doi.org\/10.3390\/e23070898","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,14]]}}}