{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T14:30:33Z","timestamp":1762353033380,"version":"build-2065373602"},"reference-count":60,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,2,7]],"date-time":"2021-02-07T00:00:00Z","timestamp":1612656000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Image semantic segmentation has been applied more and more widely in the fields of satellite remote sensing, medical treatment, intelligent transportation, and virtual reality. However, in the medical field, the study of cerebral vessel and cranial nerve segmentation based on true-color medical images is in urgent need and has good research and development prospects. We have extended the current state-of-the-art semantic-segmentation network DeepLabv3+ and used it as the basic framework. First, the feature distillation block (FDB) was introduced into the encoder structure to refine the extracted features. In addition, the atrous spatial pyramid pooling (ASPP) module was added to the decoder structure to enhance the retention of feature and boundary information. The proposed model was trained by fine tuning and optimizing the relevant parameters. Experimental results show that the encoder structure has better performance in feature refinement processing, improving target boundary segmentation precision, and retaining more feature information. Our method has a segmentation accuracy of 75.73%, which is 3% better than DeepLabv3+.<\/jats:p>","DOI":"10.3390\/s21041167","type":"journal-article","created":{"date-parts":[[2021,2,10]],"date-time":"2021-02-10T04:33:46Z","timestamp":1612931626000},"page":"1167","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Deep Neural Network-Based Semantic Segmentation of Microvascular Decompression Images"],"prefix":"10.3390","volume":"21","author":[{"given":"Ruifeng","family":"Bai","sequence":"first","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shan","family":"Jiang","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haijiang","family":"Sun","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifan","family":"Yang","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guiju","family":"Li","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"229","DOI":"10.3389\/fnins.2014.00229","article-title":"Deep learning for neuroimaging: A validation study","volume":"8","author":"Plis","year":"2014","journal-title":"Front. Neurosci."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Li, Q., Cai, W., Wang, X., Zhou, Y., Feng, D.D., and Chen, M. (2014, January 10\u201312). Medical image classification with convolutional neural network. Proceedings of the 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), Singapore.","DOI":"10.1109\/ICARCV.2014.7064414"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ypsilantis, P.P., Siddique, M., Sohn, H.M., Davies, A., Cook, G., Goh, V., and Montana, G. (2015). Predicting response to neoadjuvant chemotherapy with PET imaging using convolutional neural networks. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0137036"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Do, D.T., Le, T.Q., and Le, N.Q. (2020). Using deep neural networks and biological subwords to detect protein S-sulfenylation sites. Brief. Bioinform.","DOI":"10.1093\/bib\/bbaa128"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1162\/neco.2009.10-08-881","article-title":"Convolutional networks can learn to generate affinity graphs for image segmentation","volume":"22","author":"Turaga","year":"2010","journal-title":"Neural Comput."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Roth, H.R., Lu, L., Farag, A., Shin, H.-C., Liu, J., Turkbey, E.B., and Summers, R.M. (2015). Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation. Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-319-24553-9_68"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Roth, H.R., Lu, L., Seff, A., Cherry, K.M., Hoffman, J., Wang, S., Liu, J., Turkbey, E., and Summers, R.M. (2014). A new 2.5 D representation for lymph node detection using random sets of deep convolutional neural network observations. Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-319-10404-1_65"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Le, N.Q.K., Do, D.T., Hung, T.N.K., Lam, L.H.T., Huynh, T.-T., and Nguyen, N.T.K. (2020). A Computational Framework Based on Ensemble Deep Neural Networks for Essential Genes Identification. Int. J. Mol. Sci., 21.","DOI":"10.3390\/ijms21239070"},{"key":"ref_9","unstructured":"Koyamada, S., Shikauchi, Y., Nakae, K., Koyama, M., and Ishii, S. (2015). Deep learning of fMRI big data: A novel approach to subject-transfer decoding. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1007\/s11263-010-0344-8","article-title":"An efficient approach to semantic segmentation","volume":"95","author":"Csurka","year":"2011","journal-title":"Int. J. Comput. Vis."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s13735-017-0141-z","article-title":"A review of semantic segmentation using deep neural networks","volume":"7","author":"Guo","year":"2018","journal-title":"Int. J. Multimed. Inf. Retr."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1049\/iet-ipr.2012.0455","article-title":"Retinal vessel segmentation by improved matched filtering: Evaluation on a new high-resolution fundus image database","volume":"7","author":"Odstrcilik","year":"2013","journal-title":"IET Image Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/s00138-014-0636-z","article-title":"A self-adaptive matched filter for retinal blood vessel detection","volume":"26","author":"Chakraborti","year":"2015","journal-title":"Mach. Vis. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.cmpb.2016.03.001","article-title":"Retinal blood vessels segmentation by using Gumbel probability distribution function based matched filter","volume":"129","author":"Singh","year":"2016","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Frangi, A.F., Niessen, W.J., Vincken, K.L., and Viergever, M.A. (1998). Multiscale vessel enhancement filtering. Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/BFb0056195"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1016\/j.patcog.2012.08.009","article-title":"An effective retinal blood vessel segmentation method using multi-scale line detection","volume":"46","author":"Nguyen","year":"2013","journal-title":"Pattern Recognit."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"122","DOI":"10.4103\/2228-7477.130481","article-title":"Vessel Segmentation in Retinal Images Using Multi-scale Line Operator and K-Means Clustering","volume":"4","author":"Saffarzadeh","year":"2014","journal-title":"J. Med Signals Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.compmedimag.2015.07.006","article-title":"Retinal vessel segmentation using multi-scale textons derived from keypoints","volume":"45","author":"Zhang","year":"2015","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.bspc.2018.06.007","article-title":"Automatic multiscale vascular image segmentation algorithm for coronary angiography","volume":"46","author":"Carballal","year":"2018","journal-title":"Biomed. Signal Process. Control."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Khawaja, A., Khan, T.M., Khan, M.A., and Syed, J.N. (2019). A Multi-Scale Directional Line Detector for Retinal Vessel Segmentation. Sensors, 19.","DOI":"10.3390\/s19224949"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1007\/s10916-010-9466-3","article-title":"Morphological Multiscale Enhancement, Fuzzy Filter and Watershed for Vascular Tree Extraction in Angiogram","volume":"35","author":"Sun","year":"2011","journal-title":"J. Med Syst."},{"key":"ref_22","unstructured":"Kass, M., Witkin, A., and Tetzopoulos, D. (1998, January 23\u201325). Active contour models. International Journal of computer vision. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Santa Barbara, CA, USA."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.neucom.2016.07.077","article-title":"Saliency driven vasculature segmentation with infinite perimeter active contour model","volume":"259","author":"Zhao","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1080\/09687630801889440","article-title":"Comparison of active contour models for image segmentation in X-ray coronary angiogram images","volume":"32","author":"Devi","year":"2008","journal-title":"J. Med Eng. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Tagizaheh, M., Sadri, S., and Doosthoseini, A.M. (2011, January 16\u201317). Segmentation of coronary vessels by combining the detection of centerlines and active contour model. Proceedings of the 2011 7th Iranian Conference on Machine Vision and Image Processing, Tehran, Iran.","DOI":"10.1109\/IranianMVIP.2011.6121536"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhao, S., Liu, Z., Tian, Y., Duan, F., and Pan, Y. (2016). An active contour model based on adaptive threshold for extraction of cerebral vascular structures. Comput. Math. Methods Med., 2016.","DOI":"10.1155\/2016\/6472397"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Brieva, J., Gonzalez, E., Gonzalez, F., Bousse, A., and Bellanger, J. (2005, January 1\u20134). A level set method for vessel segmentation in coronary angiography. Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China.","DOI":"10.1109\/IEMBS.2005.1615949"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1109\/TBME.2007.896587","article-title":"Vessel extraction under non-uniform illumination: A level set approach","volume":"55","author":"Sum","year":"2007","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1186\/1475-925X-13-169","article-title":"3D vasculature segmentation using localized hybrid level-set method","volume":"13","author":"Hong","year":"2014","journal-title":"Biomed. Eng. Online"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.compbiomed.2015.09.008","article-title":"Segmentation of retinal vessels by means of directional response vector similarity and region growing","volume":"66","author":"Hajdu","year":"2015","journal-title":"Comput. Biol. Med."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1738","DOI":"10.1109\/TBME.2015.2403295","article-title":"Iterative vessel segmentation of fundus images","volume":"62","author":"Roychowdhury","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Lara, D.S., Faria, A.W., Ara\u00fajo, A.d.A., and Menotti, D. (2009, January 11\u201315). A semi-automatic method for segmentation of the coronary artery tree from angiography. Proceedings of the 2009 XXII Brazilian Symposium on Computer Graphics and Image Processing, Rio De Janiero, Brazil.","DOI":"10.1109\/SIBGRAPI.2009.41"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1186\/1475-925X-9-40","article-title":"Automatic segmentation of coronary angiograms based on fuzzy inferring and probabilistic tracking","volume":"9","author":"Shoujun","year":"2010","journal-title":"Biomed. Eng. Online"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.cmpb.2018.01.002","article-title":"Automated coronary artery tree segmentation in X-ray angiography using improved Hessian based enhancement and statistical region merging","volume":"157","author":"Wan","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_36","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_37","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Nasr-Esfahani, E., Samavi, S., Karimi, N., Soroushmehr, S.R., Ward, K., Jafari, M.H., Felfeliyan, B., Nallamothu, B., and Najarian, K. (2016, January 16\u201320). Vessel extraction in X-ray angiograms using deep learning. Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7590784"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Phellan, R., Peixinho, A., Falc\u00e3o, A., and Forkert, N.D. (2017). Vascular segmentation in tof mra images of the brain using a deep convolutional neural network. Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, Springer.","DOI":"10.1007\/978-3-319-67534-3_5"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2181","DOI":"10.1007\/s11548-017-1619-0","article-title":"Multi-level deep supervised networks for retinal vessel segmentation","volume":"12","author":"Mo","year":"2017","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compmedimag.2018.04.005","article-title":"Retinal blood vessel segmentation using fully convolutional network with transfer learning","volume":"68","author":"Jiang","year":"2018","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.cmpb.2019.06.030","article-title":"Scale-space approximated convolutional neural networks for retinal vessel segmentation","volume":"178","author":"Noh","year":"2019","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"97","DOI":"10.3389\/fnins.2019.00097","article-title":"A U-Net Deep Learning Framework for High Performance Vessel Segmentation in Patients With Cerebrovascular Disease","volume":"13","author":"Livne","year":"2019","journal-title":"Front. Neurosci."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial pyramid pooling in deep convolutional networks for visual recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_49","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L. (2014). Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_51","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2295","DOI":"10.1109\/JPROC.2017.2761740","article-title":"Efficient processing of deep neural networks: A tutorial and survey","volume":"105","author":"Sze","year":"2017","journal-title":"Proc. IEEE"},{"key":"ref_54","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_55","doi-asserted-by":"crossref","unstructured":"Hui, Z., Wang, X., and Gao, X. (2018, January 18\u201322). Fast and accurate single image super-resolution via information distillation network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00082"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Hui, Z., Gao, X., Yang, Y., and Wang, X. (2019, January 21\u201325). Lightweight image super-resolution with information multi-distillation network. Proceedings of the 27th ACM International Conference on Multimedia, Nice, France.","DOI":"10.1145\/3343031.3351084"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Liu, J., Tang, J., and Wu, G. (2020). Residual feature distillation network for lightweight image super-resolution. arXiv.","DOI":"10.1109\/CVPR42600.2020.00243"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2019, January 15\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_60","unstructured":"Wu, H., Zhang, J., Huang, K., Liang, K., and Fastfcn, Y.Y. (2019). Rethinking dilated convolution in the backbone for semantic segmentation. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/4\/1167\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:20:51Z","timestamp":1760160051000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/4\/1167"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,7]]},"references-count":60,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["s21041167"],"URL":"https:\/\/doi.org\/10.3390\/s21041167","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2021,2,7]]}}}