{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T10:49:57Z","timestamp":1761648597891,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2018,10,9]],"date-time":"2018-10-09T00:00:00Z","timestamp":1539043200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"General Program of National Natural Science Foundation of China (NSFC)","award":["61572362","81571347"],"award-info":[{"award-number":["61572362","81571347"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["22120180012"],"award-info":[{"award-number":["22120180012"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Automatic reconstructing of neural circuits in the brain is one of the most crucial studies in neuroscience. Connectomes segmentation plays an important role in reconstruction from electron microscopy (EM) images; however, it is rather challenging due to highly anisotropic shapes with inferior quality and various thickness. In our paper, we propose a novel connectomes segmentation framework called adversarial and densely dilated network (ADDN) to address these issues. ADDN is based on the conditional Generative Adversarial Network (cGAN) structure which is the latest advance in machine learning with power to generate images similar to the ground truth especially when the training data is limited. Specifically, we design densely dilated network (DDN) as the segmentor to allow a deeper architecture and larger receptive fields for more accurate segmentation. Discriminator is trained to distinguish generated segmentation from manual segmentation. During training, such adversarial loss function is optimized together with dice loss. Extensive experimental results demonstrate that our ADDN is effective for such connectomes segmentation task, helping to retrieve more accurate segmentation and attenuate the blurry effects of generated boundary map. Our method obtains state-of-the-art performance while requiring less computation on ISBI 2012 EM dataset and mouse piriform cortex dataset.<\/jats:p>","DOI":"10.3390\/sym10100467","type":"journal-article","created":{"date-parts":[[2018,10,9]],"date-time":"2018-10-09T11:10:44Z","timestamp":1539083444000},"page":"467","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["An Adversarial and Densely Dilated Network for Connectomes Segmentation"],"prefix":"10.3390","volume":"10","author":[{"given":"Ke","family":"Chen","sequence":"first","affiliation":[{"name":"School of Software Enginnering, Tongji University, Shanghai 201804, China"}]},{"given":"Dandan","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Software Enginnering, Tongji University, Shanghai 201804, China"}]},{"given":"Jianwei","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Software Enginnering, Tongji University, Shanghai 201804, China"},{"name":"Institute of Translational Medicine, Tongji University, Shanghai 201804, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7052-7268","authenticated-orcid":false,"given":"Ye","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Software Enginnering, Tongji University, Shanghai 201804, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,10,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sporns, O., Tononi, G., and K\u00f6tter, R. (2005). The human connectome: A structural description of the human brain. PLoS Comput. Biol., 1.","DOI":"10.1371\/journal.pcbi.0010042"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Cardona, A., Saalfeld, S., Preibisch, S., Schmid, B., Cheng, A., Pulokas, J., Tomancak, P., and Hartenstein, V. (2010). An integrated micro-and macroarchitectural analysis of the Drosophila brain by computer-assisted serial section electron microscopy. PLoS Biol., 8.","DOI":"10.1371\/journal.pbio.1000502"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Jurrus, E., Whitaker, R., Jones, B.W., Marc, R., and Tasdizen, T. (2008, January 14\u201317). An optimal-path approach for neural circuit reconstruction. Proceedings of the 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Paris, France.","DOI":"10.1109\/ISBI.2008.4541320"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"12101","DOI":"10.1523\/JNEUROSCI.3994-06.2006","article-title":"Uniform serial sectioning for transmission electron microscopy","volume":"26","author":"Harris","year":"2006","journal-title":"J. Neurosci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1016\/j.media.2010.06.002","article-title":"Detection of neuron membranes in electron microscopy images using a serial neural network architecture","volume":"14","author":"Jurrus","year":"2010","journal-title":"Med. Image Anal."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Seyedhosseini, M., Kumar, R., Jurrus, E., Giuly, R., Ellisman, M., Pfister, H., and Tasdizen, T. (2011, January 18\u201322). Detection of neuron membranes in electron microscopy images using multi-scale context and radon-like features. Proceedings of the International Conference on Medical Image Computing and Computer-assisted Intervention, Toronto, ON, Canada.","DOI":"10.1007\/978-3-642-23623-5_84"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Jain, V., Murray, J.F., Roth, F., Turaga, S., Zhigulin, V., Briggman, K.L., Helmstaedter, M.N., Denk, W., and Seung, H.S. (2007, January 14\u201321). Supervised learning of image restoration with convolutional networks. Proceedings of the 2007 IEEE 11th International Conference on Computer Vision, Rio de Janeiro, Brazil.","DOI":"10.1109\/ICCV.2007.4408909"},{"key":"ref_8","unstructured":"Ciresan, D., Giusti, A., Gambardella, L.M., and Schmidhuber, J. (2012). Deep neural networks segment neuronal membranes in electron microscopy images. Adv. Neural Inf. Proc. Syst., Available online: http:\/\/papers.nips.cc\/paper\/4741-deep-neural-networks."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 8\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":"Chen, H., Qi, X., Cheng, J.Z., and Heng, P.A. (2016, January 12\u201317). Deep Contextual Networks for Neuronal Structure Segmentation. Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA.","DOI":"10.1609\/aaai.v30i1.10141"},{"key":"ref_11","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_12","unstructured":"Quan, T.M., Hilderbrand, D.G., and Jeong, W.K. (arXiv, 2016). FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics, arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Drozdzal, M., Vorontsov, E., Chartrand, G., Kadoury, S., and Pal, C. (2016, January 21). The importance of skip connections in biomedical image segmentation. Proceedings of the International Workshop on Deep Learning in Medical Image Analysis, Athens, Greece.","DOI":"10.1007\/978-3-319-46976-8_19"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1109\/TMI.2016.2613019","article-title":"Residual deconvolutional networks for brain electron microscopy image segmentation","volume":"36","author":"Fakhry","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhao, X., Wu, Y., Song, G., Li, Z., Fan, Y., and Zhang, Y. (2016, January 17). Brain tumor segmentation using a fully convolutional neural network with conditional random fields. Proceedings of the International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Athens, Greece.","DOI":"10.1007\/978-3-319-55524-9_8"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Johnson, J., Alahi, A., and Fei-Fei, L. (2016, January 8\u201316). Perceptual losses for real-time style transfer and super-resolution. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., and Efros, A.A. (2018, August 10). Image-to-image translation with conditional adversarial networks. Available online: http:\/\/openaccess.thecvf.com\/content_cvpr_2017\/papers\/Isola_Image-To-Image_Translation_With_CVPR_2017_paper.pdf.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Rezaei, M., Harmuth, K., Gierke, W., Kellermeier, T., Fischer, M., Yang, H., and Meinel, C. (arXiv, 2017). Conditional Adversarial Network for Semantic Segmentation of Brain Tumor, arXiv.","DOI":"10.1007\/978-3-319-75238-9_21"},{"key":"ref_19","unstructured":"Yu, F., and Koltun, V. (arXiv, 2015). Multi-scale context aggregation by dilated convolutions, arXiv."},{"key":"ref_20","first-page":"3","article-title":"Densely Connected Convolutional Networks","volume":"1","author":"Huang","year":"2017","journal-title":"CVPR"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"J\u00e9gou, S., Drozdzal, M., Vazquez, D., Romero, A., and Bengio, Y. (2017, January 21\u201326). The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.156"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/0021-9991(88)90002-2","article-title":"Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations","volume":"79","author":"Osher","year":"1988","journal-title":"J. Comput. Phys."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"546","DOI":"10.1109\/TCYB.2015.2409119","article-title":"A level set approach to image segmentation with intensity inhomogeneity","volume":"46","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Cybern."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1547","DOI":"10.1016\/j.patcog.2014.10.018","article-title":"An intensity-texture model based level set method for image segmentation","volume":"48","author":"Min","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cviu.2017.03.007","article-title":"Superpixels: An evaluation of the state-of-the-art","volume":"166","author":"Stutz","year":"2018","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.jvcir.2015.09.015","article-title":"Automated coronal hole segmentation from Solar EUV Images using the watershed transform","volume":"33","author":"Ciecholewski","year":"2015","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"925","DOI":"10.1109\/TPAMI.2009.71","article-title":"Watershed cuts: Thinnings, shortest path forests, and topological watersheds","volume":"32","author":"Cousty","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2290","DOI":"10.1109\/TPAMI.2009.96","article-title":"Turbopixels: Fast superpixels using geometric flows","volume":"31","author":"Levinshtein","year":"2009","journal-title":"IIEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","unstructured":"Vazquez, L., Sapiro, G., and Randall, G. (1998, January 7). Segmenting neurons in electronic microscopy via geometric tracing. Proceedings of the 1998 International Conference on Image Processing (ICIP98), Chicago, IL, USA."},{"key":"ref_30","unstructured":"Vu, N., and Manjunath, B. (2008, January 12\u201315). Graph cut segmentation of neuronal structures from transmission electron micrographs. Proceedings of the 2008 15th IEEE International Conference on Image Processing, San Diego, CA, USA."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Kaynig, V., Fuchs, T.J., and Buhmann, J.M. (2010, January 18\u201322). Geometrical consistent 3D tracing of neuronal processes in ssTEM data. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Toronto, ON, Canada.","DOI":"10.1007\/978-3-642-15745-5_26"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Nekrasov, V., Ju, J., and Choi, J. (arXiv, 2016). Global deconvolutional networks for semantic segmentation, arXiv.","DOI":"10.5244\/C.30.124"},{"key":"ref_33","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 2016 IEEE conference on computer vision and pattern recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.media.2017.11.005","article-title":"Learning Normalized Inputs for Iterative Estimation in Medical Image Segmentation","volume":"44","author":"Drozdzal","year":"2018","journal-title":"Med. Image Anal."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhang, H., Xu, T., Li, H., Zhang, S., Huang, X., Wang, X., and Metaxas, D. (2018, August 10). Stackgan: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks. Available online: http:\/\/openaccess.thecvf.com\/content_ICCV_2017\/papers\/Zhang_StackGAN_Text_to_ICCV_2017_paper.pdf.","DOI":"10.1109\/ICCV.2017.629"},{"key":"ref_36","first-page":"4","article-title":"Photo-realistic single image super-resolution using a generative adversarial network","volume":"1","author":"Ledig","year":"2016","journal-title":"CVPR"},{"key":"ref_37","unstructured":"Luc, P., Couprie, C., Chintala, S., and Verbeek, J. (arXiv, 2016). Semantic segmentation using adversarial networks, arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Moeskops, P., Veta, M., Lafarge, M.W., Eppenhof, K.A., and Pluim, J.P. (2017, January 14). Adversarial training and dilated convolutions for brain MRI segmentation. Proceedings of the International Workshop on Deep Learning in Medical Image Analysis, Qu\u00e9bec, QC, Canada.","DOI":"10.1007\/978-3-319-67558-9_7"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Li, Z., Wang, Y., and Yu, J. (2017, January 14). Brain Tumor Segmentation Using an Adversarial Network. Proceedings of the International MICCAI Brainlesion Workshop, Quebec, QC, Canada.","DOI":"10.1007\/978-3-319-75238-9_11"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Dai, W., Doyle, J., Liang, X., Zhang, H., Dong, N., Li, Y., and Xing, E.P. (arXiv, 2017). Scan: Structure correcting adversarial network for chest x-rays organ segmentation, arXiv.","DOI":"10.1007\/978-3-030-00889-5_30"},{"key":"ref_41","unstructured":"Kohl, S., Bonekamp, D., Schlemmer, H.P., Yaqubi, K., Hohenfellner, M., Hadaschik, B., Radtke, J.P., and Maier-Hein, K. (arXiv, 2017). Adversarial Networks for the Detection of Aggressive Prostate Cancer, arXiv."},{"key":"ref_42","unstructured":"Mirza, M., and Osindero, S. (arXiv, 2014). Conditional generative adversarial nets, arXiv."},{"key":"ref_43","first-page":"142","article-title":"Crowdsourcing the creation of image segmentation algorithms for connectomics","volume":"9","author":"Turaga","year":"2015","journal-title":"Front. Neuroanat."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 11\u201318). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the IEEE international conference on computer vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_45","unstructured":"Chollet, F. (2018, August 10). Keras: Deep Learning Library for Theano and Tensorflow. Available online: https:\/\/keras. io\/k."},{"key":"ref_46","unstructured":"Lee, K., Zlateski, A., Ashwin, V., and Seung, H.S. (2015, January 7\u201312). Recursive training of 2d-3d convolutional networks for neuronal boundary prediction. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_47","unstructured":"Simard, P.Y., Steinkraus, D., and Platt, J.C. (2003, January 3\u20136). Best practices for convolutional neural networks applied to visual document analysis. Proceedings of the Seventh International Conference on Document Analysis and Recognition, Edinburgh, UK."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Shen, W., Wang, B., Jiang, Y., Wang, Y., and Yuille, A. (arXiv, 2017). Multi-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detection, arXiv.","DOI":"10.1109\/ICCV.2017.262"},{"key":"ref_49","unstructured":"Pleiss, G., Chen, D., Huang, G., Li, T., van der Maaten, L., and Weinberger, K.Q. (arXiv, 2017). Memory-efficient implementation of densenets, arXiv."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/10\/10\/467\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:24:34Z","timestamp":1760196274000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/10\/10\/467"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,10,9]]},"references-count":49,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2018,10]]}},"alternative-id":["sym10100467"],"URL":"https:\/\/doi.org\/10.3390\/sym10100467","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2018,10,9]]}}}