{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T17:08:17Z","timestamp":1779383297254,"version":"3.53.1"},"reference-count":39,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,3,4]],"date-time":"2020-03-04T00:00:00Z","timestamp":1583280000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61671272"],"award-info":[{"award-number":["61671272"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Opening Project of Guangdong Province Key Laboratory of Big Data Analysis 333 and Processing","award":["201803"],"award-info":[{"award-number":["201803"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Optical coherence tomography (OCT) is an optical high-resolution imaging technique for ophthalmic diagnosis. In this paper, we take advantages of multi-scale input, multi-scale side output and dual attention mechanism and present an enhanced nested U-Net architecture (MDAN-UNet), a new powerful fully convolutional network for automatic end-to-end segmentation of OCT images. We have evaluated two versions of MDAN-UNet (MDAN-UNet-16 and MDAN-UNet-32) on two publicly available benchmark datasets which are the Duke Diabetic Macular Edema (DME) dataset and the RETOUCH dataset, in comparison with other state-of-the-art segmentation methods. Our experiment demonstrates that MDAN-UNet-32 achieved the best performance, followed by MDAN-UNet-16 with smaller parameter, for multi-layer segmentation and multi-fluid segmentation respectively.<\/jats:p>","DOI":"10.3390\/a13030060","type":"journal-article","created":{"date-parts":[[2020,3,6]],"date-time":"2020-03-06T07:33:46Z","timestamp":1583480026000},"page":"60","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":72,"title":["MDAN-UNet: Multi-Scale and Dual Attention Enhanced Nested U-Net Architecture for Segmentation of Optical Coherence Tomography Images"],"prefix":"10.3390","volume":"13","author":[{"given":"Wen","family":"Liu","sequence":"first","affiliation":[{"name":"School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7155-8261","authenticated-orcid":false,"given":"Yankui","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tsinghua University, 30 Shuangqing Road, Beijing 100084, China"},{"name":"Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou 510006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingge","family":"Ji","sequence":"additional","affiliation":[{"name":"School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China"},{"name":"Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou 510006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1178","DOI":"10.1126\/science.1957169","article-title":"Optical coherence tomography","volume":"254","author":"Huang","year":"1991","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.preteyeres.2015.07.007","article-title":"A paradigm shift in imaging biomarkers in neovascular age-related macular degeneration","volume":"50","author":"Waldstein","year":"2016","journal-title":"Prog. Retin. Eye Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/s12020-007-0040-9","article-title":"How the diabetic eye loses vision","volume":"32","author":"Davidson","year":"2007","journal-title":"Endocrine"},{"key":"ref_4","first-page":"15","article-title":"A review of algorithms for segmentation of retinal image data using optical coherence tomography","volume":"1","author":"DeBuc","year":"2011","journal-title":"Image Segm."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.preteyeres.2018.07.004","article-title":"Artificial intelligence in retina","volume":"67","author":"Sadeghipour","year":"2018","journal-title":"Prog. Retin. Eye Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1172","DOI":"10.1364\/BOE.6.001172","article-title":"Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema","volume":"6","author":"Chiu","year":"2015","journal-title":"Biomed. Opt. Express"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2888","DOI":"10.1364\/BOE.7.002888","article-title":"Learning layer-specific edges for segmenting retinal layers with large deformations","volume":"7","author":"Karri","year":"2016","journal-title":"Biomed. Opt. Express"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1874","DOI":"10.1364\/BOE.8.001874","article-title":"Joint retinal layer and fluid segmentation in OCT scans of eyes with severe macular edema using unsupervised representation and auto-context","volume":"8","author":"Montuoro","year":"2017","journal-title":"Biomed. Opt. Express"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Devalla, S.K., Renukanand, P.K., Sreedhar, B.K., Perera, S., Mari, J.M., Chin, K.S., Tun, T.A., Strouthidis, N.G., Aung, T., and Thi\u00e9ry, A.H. (2018). DRUNET: A dilated-residual u-net deep learning network to digitally stain optic nerve head tissues in optical coherence tomography images. arXiv.","DOI":"10.1364\/BOE.9.003244"},{"key":"ref_12","unstructured":"Zadeh, S.G., Wintergerst, M.W., Wiens, V., Thiele, S., Holz, F.G., Finger, R.P., and Schultz, T. (2017). CNNs enable accurate and fast segmentation of drusen in optical coherence tomography. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1545","DOI":"10.1364\/BOE.9.001545","article-title":"Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optical coherence tomography","volume":"9","author":"Venhuizen","year":"2018","journal-title":"Biomed. Opt. Express"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"105830","DOI":"10.1016\/j.optlastec.2019.105830","article-title":"Automated segmentation of fluid regions in optical coherence tomography B-scan images of age-related macular degeneration","volume":"122","author":"Chen","year":"2020","journal-title":"Opt. Laser Technol."},{"key":"ref_15","unstructured":"Ben-Cohen, A., Mark, D., Kovler, I., Zur, D., Barak, A., Iglicki, M., and Soferman, R. (2020, February 29). Retinal layers segmentation using fully convolutional network in OCT images. Available online: https:\/\/www.rsipvision.com\/wp-content\/uploads\/2017\/06\/Retinal-Layers-Segmentation.pdf."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.media.2019.02.011","article-title":"Deep-learning based multiclass retinal fluid segmentation and detection in optical coherence tomography images using a fully convolutional neural network","volume":"54","author":"Lu","year":"2019","journal-title":"Med Image Anal."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3627","DOI":"10.1364\/BOE.8.003627","article-title":"ReLayNet: Retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks","volume":"8","author":"Roy","year":"2017","journal-title":"Biomed. Opt. Express"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2281","DOI":"10.1109\/TMI.2019.2903562","article-title":"CE-Net: Context encoder network for 2D medical image segmentation","volume":"38","author":"Gu","year":"2019","journal-title":"IEEE Trans. Med Imaging"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., and Liang, J. (2018). Unet++: A nested u-net architecture for medical image segmentation. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"ref_22","unstructured":"Lee, C.-Y., Xie, S., Gallagher, P., Zhang, Z., and Tu, Z. (2015, January 9\u201312). Deeply-supervised nets. Proceedings of the 18th International Conference on Artificial Intelligence and Statistics, San Diego, CA, USA."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1597","DOI":"10.1109\/TMI.2018.2791488","article-title":"Joint optic disc and cup segmentation based on multi-label deep network and polar transformation","volume":"37","author":"Fu","year":"2018","journal-title":"IEEE Trans. Med Imaging"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Abraham, N., and Khan, N.M. (2019, January 8\u201311). A novel focal tversky loss function with improved attention u-net for lesion segmentation. Proceedings of the 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy.","DOI":"10.1109\/ISBI.2019.8759329"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Xie, S., and Tu, Z. (2015, January 11\u201318). Holistically-nested edge detection. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.164"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chu, X., Yang, W., Ouyang, W., Ma, C., Yuille, A.L., and Wang, X. (2017, January 21\u201326). Multi-context attention for human pose estimation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.601"},{"key":"ref_27","unstructured":"Li, H., Xiong, P., An, J., and Wang, L. (2018). Pyramid attention network for semantic segmentation. arXiv."},{"key":"ref_28","unstructured":"Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., and Kainz, B. (2018). Attention u-net: Learning where to look for the pancreas. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"702","DOI":"10.1016\/j.patcog.2018.12.021","article-title":"Deep gated attention networks for large-scale street-level scene segmentation","volume":"88","author":"Zhang","year":"2019","journal-title":"Pattern Recognit."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2019, January 16\u201320). Dual attention network for scene segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., Krishnan, D., Taylor, G.W., and Fergus, R. (2010, January 13\u201318). Deconvolutional networks. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539957"},{"key":"ref_32","unstructured":"Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R.R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv."},{"key":"ref_33","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_34","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"},{"key":"ref_35","unstructured":"Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A. (2020, February 29). Automatic differentiation in pytorch. Available online: https:\/\/openreview.net\/pdf?id=BJJsrmfCZ."},{"key":"ref_36","unstructured":"Kingma, D.P. (2015). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_37","unstructured":"Simard, P.Y., Steinkraus, D., and Platt, J.C. (2003, January 6). Best practices for convolutional neural networks applied to visual document analysis. Proceedings of the 7th International Conference on Document Analysis and Recognition, Edinburgh, UK."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1858","DOI":"10.1109\/TMI.2019.2901398","article-title":"RETOUCH: The Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge","volume":"38","author":"Venhuizen","year":"2019","journal-title":"IEEE Trans. Med Imaging"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Tennakoon, R., Gostar, A.K., Hoseinnezhad, R., and Bab-Hadiashar, A. (2017, January 10\u201314). Retinal fluid segmentation and classification in OCT images using adversarial loss based CNN. Proceedings of the MICCAI Retinal OCT Fluid Challenge (RETOUCH), Quebec, QC, Canada.","DOI":"10.1109\/ISBI.2018.8363842"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/13\/3\/60\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:04:11Z","timestamp":1760173451000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/13\/3\/60"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,4]]},"references-count":39,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["a13030060"],"URL":"https:\/\/doi.org\/10.3390\/a13030060","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,4]]}}}