{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T10:10:15Z","timestamp":1767262215614,"version":"build-2065373602"},"reference-count":58,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,6,18]],"date-time":"2020-06-18T00:00:00Z","timestamp":1592438400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2019R1A2C1083813","NRF-2018R1D1A1B07041921","NRF-2019R1F1A1041123"],"award-info":[{"award-number":["NRF-2019R1A2C1083813","NRF-2018R1D1A1B07041921","NRF-2019R1F1A1041123"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Ophthalmological analysis plays a vital role in the diagnosis of various eye diseases, such as glaucoma, retinitis pigmentosa (RP), and diabetic and hypertensive retinopathy. RP is a genetic retinal disorder that leads to progressive vision degeneration and initially causes night blindness. Currently, the most commonly applied method for diagnosing retinal diseases is optical coherence tomography (OCT)-based disease analysis. In contrast, fundus imaging-based disease diagnosis is considered a low-cost diagnostic solution for retinal diseases. This study focuses on the detection of RP from the fundus image, which is a crucial task because of the low quality of fundus images and non-cooperative image acquisition conditions. Automatic detection of pigment signs in fundus images can help ophthalmologists and medical practitioners in diagnosing and analyzing RP disorders. To accurately segment pigment signs for diagnostic purposes, we present an automatic RP segmentation network (RPS-Net), which is a specifically designed deep learning-based semantic segmentation network to accurately detect and segment the pigment signs with fewer trainable parameters. Compared with the conventional deep learning methods, the proposed method applies a feature enhancement policy through multiple dense connections between the convolutional layers, which enables the network to discriminate between normal and diseased eyes, and accurately segment the diseased area from the background. Because pigment spots can be very small and consist of very few pixels, the RPS-Net provides fine segmentation, even in the case of degraded images, by importing high-frequency information from the preceding layers through concatenation inside and outside the encoder-decoder. To evaluate the proposed RPS-Net, experiments were performed based on 4-fold cross-validation using the publicly available Retinal Images for Pigment Signs (RIPS) dataset for detection and segmentation of retinal pigments. Experimental results show that RPS-Net achieved superior segmentation performance for RP diagnosis, compared with the state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/s20123454","type":"journal-article","created":{"date-parts":[[2020,6,18]],"date-time":"2020-06-18T12:21:46Z","timestamp":1592482906000},"page":"3454","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Deep Learning-Based Detection of Pigment Signs for Analysis and Diagnosis of Retinitis Pigmentosa"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1868-5207","authenticated-orcid":false,"given":"Muhammad","family":"Arsalan","sequence":"first","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea"}]},{"given":"Na Rae","family":"Baek","sequence":"additional","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7679-081X","authenticated-orcid":false,"given":"Muhammad","family":"Owais","sequence":"additional","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1691-9532","authenticated-orcid":false,"given":"Tahir","family":"Mahmood","sequence":"additional","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea"}]},{"given":"Kang Ryoung","family":"Park","sequence":"additional","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100203","DOI":"10.1016\/j.cosrev.2019.100203","article-title":"Application of deep learning for retinal image analysis: A review","volume":"35","author":"Badar","year":"2020","journal-title":"Comput. Sci. Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"748","DOI":"10.1111\/aos.13141","article-title":"A review of the mechanisms of cone degeneration in retinitis pigmentosa","volume":"94","author":"Narayan","year":"2016","journal-title":"Acta Ophthalmol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"S147","DOI":"10.1097\/IAE.0000000000001256","article-title":"Multimodal imaging of disease-associated pigmentary changes in retinitis pigmentosa","volume":"36","author":"Schuerch","year":"2016","journal-title":"Retina"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Limoli, P.G., Vingolo, E.M., Limoli, C., and Nebbioso, M. (2019). Stem cell surgery and growth factors in retinitis pigmentosa patients: Pilot study after literature review. Biomedicines, 7.","DOI":"10.20944\/preprints201907.0347.v1"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1080\/21678707.2020.1735352","article-title":"Monitoring progression of retinitis pigmentosa: Current recommendations and recent advances","volume":"8","author":"Menghini","year":"2020","journal-title":"Expert Opin. Orphan Drugs"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.ophtha.2019.05.029","article-title":"Development and validation of deep learning models for screening multiple abnormal findings in retinal fundus images","volume":"127","author":"Son","year":"2020","journal-title":"Ophthalmology"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Das, A., Giri, R., Chourasia, G., and Bala, A.A. (2019, January 17\u201319). Classification of retinal diseases using transfer learning approach. Proceedings of the International Conference on Communication and Electronics Systems, Coimbatore, India.","DOI":"10.1109\/ICCES45898.2019.9002415"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"29299","DOI":"10.1109\/ACCESS.2020.2972318","article-title":"Improving the performance of convolutional neural network for the segmentation of optic disc in fundus images using attention gates and conditional random fields","volume":"8","author":"Bhatkalkar","year":"2020","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Maji, D., Santara, A., Ghosh, S., Sheet, D., and Mitra, P. (2015, January 25\u201329). Deep neural network and random forest hybrid architecture for learning to detect retinal vessels in fundus images. Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Milan, Italy.","DOI":"10.1109\/EMBC.2015.7319030"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"122634","DOI":"10.1109\/ACCESS.2019.2935138","article-title":"A fundus retinal vessels segmentation scheme based on the improved deep learning U-Net model","volume":"7","author":"Xiuqin","year":"2019","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.bspc.2019.01.022","article-title":"A coarse-to-fine deep learning framework for optic disc segmentation in fundus images","volume":"51","author":"Wang","year":"2019","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kim, J., Tran, L., Chew, E.Y., and Antani, S. (2019, January 5\u20137). Optic disc and cup segmentation for glaucoma characterization using deep learning. Proceedings of the IEEE 32nd International Symposium on Computer-Based Medical Systems, Cordoba, Spain.","DOI":"10.1109\/CBMS.2019.00100"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Edupuganti, V.G., Chawla, A., and Kale, A. (2018, January 7\u201310). Automatic optic disk and cup segmentation of fundus images using deep learning. Proceedings of the 25th IEEE International Conference on Image Processing, Athens, Greece.","DOI":"10.1109\/ICIP.2018.8451753"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.eswa.2018.12.008","article-title":"Multi-parametric optic disc segmentation using superpixel based feature classification","volume":"120","author":"Rehman","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Arsalan, M., Owais, M., Mahmood, T., Cho, S.W., and Park, K.R. (2019). Aiding the diagnosis of diabetic and hypertensive retinopathy using artificial intelligence-based semantic segmentation. J. Clin. Med., 8.","DOI":"10.3390\/jcm8091446"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1595","DOI":"10.1136\/bjophthalmol-2018-313669","article-title":"Classification of disease severity in retinitis pigmentosa","volume":"103","author":"Iftikhar","year":"2019","journal-title":"Br. J. Ophthalmol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-56155-x","article-title":"Non-contact smartphone-based fundus imaging compared to conventional fundus imaging: A low-cost alternative for retinopathy of prematurity screening and documentation","volume":"9","author":"Wintergerst","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_18","unstructured":"Vaidehi, K., and Srilatha, J. (2019, January 15\u201316). A review on automatic glaucoma detection in retinal fundus images. Proceedings of the 3rd International Conference on Data Engineering and Communication Technology, Hyderabad, India."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yan, Z., Han, X., Wang, C., Qiu, Y., Xiong, Z., and Cui, S. (2019, January 8\u201311). Learning mutually local-global U-Nets for high-resolution retinal lesion segmentation in fundus images. Proceedings of the IEEE 16th International Symposium on Biomedical Imaging, Venice, Italy.","DOI":"10.1109\/ISBI.2019.8759579"},{"key":"ref_20","unstructured":"Agarwal, B., Balas, V.E., Jain, L.C., Poonia, R.C. (2020). Deep Learning Techniques for Biomedical and Health Informatics, Academic Press."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.patrec.2019.11.042","article-title":"Integrated design of deep features fusion for localization and classification of skin cancer","volume":"131","author":"Amin","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Owais, M., Arsalan, M., Choi, J., Mahmood, T., and Park, K.R. (2019). Artificial intelligence-based classification of multiple gastrointestinal diseases using endoscopy videos for clinical diagnosis. J. Clin. Med., 8.","DOI":"10.3390\/jcm8070986"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Owais, M., Arsalan, M., Choi, J., and Park, K.R. (2019). Effective diagnosis and treatment through content-based medical image retrieval (CBMIR) by using artificial intelligence. J. Clin. Med., 8.","DOI":"10.3390\/jcm8040462"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Mahmood, T., Arsalan, M., Owais, M., Lee, M.B., and Park, K.R. (2020). Artificial intelligence-based mitosis detection in breast cancer histopathology images using faster R-CNN and deep CNNs. J. Clin. Med., 9.","DOI":"10.3390\/jcm9030749"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Arsalan, M., Owais, M., Mahmood, T., Choi, J., and Park, K.R. (2020). Artificial intelligence-based diagnosis of cardiac and related diseases. J. Clin. Med., 9.","DOI":"10.3390\/jcm9030871"},{"key":"ref_26","unstructured":"(2020, March 30). RPS-Net Model with Algoritm. Available online: http:\/\/dm.dgu.edu\/link.html."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.medengphy.2007.04.010","article-title":"A novel automatic image processing algorithm for detection of hard exudates based on retinal image analysis","volume":"30","author":"Hornero","year":"2008","journal-title":"Med. Eng. Phys."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1016\/j.media.2014.05.004","article-title":"Exudate detection in color retinal images for mass screening of diabetic retinopathy","volume":"18","author":"Zhang","year":"2014","journal-title":"Med. Image Anal."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.compmedimag.2009.10.001","article-title":"A coarse-to-fine strategy for automatically detecting exudates in color eye fundus images","volume":"34","author":"Welfer","year":"2010","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"16410","DOI":"10.1364\/OE.16.016410","article-title":"Retinal pigment epithelium segmentation by polarization sensitive optical coherence tomography","volume":"16","author":"Pircher","year":"2008","journal-title":"Opt. Express"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2493","DOI":"10.1364\/BOE.2.002493","article-title":"Automated segmentation of outer retinal layers in macular OCT images of patients with retinitis pigmentosa","volume":"2","author":"Yang","year":"2011","journal-title":"Biomed. Opt. Express"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Das, H., Saha, A., and Deb, S. (2014, January 7\u20138). An expert system to distinguish a defective eye from a normal eye. Proceedings of the International Conference on Issues and Challenges in Intelligent Computing Techniques, Ghaziabad, India.","DOI":"10.1109\/ICICICT.2014.6781270"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"519","DOI":"10.24425\/ijet.2019.129808","article-title":"Diagnosis of retinitis pigmentosa from retinal images","volume":"65","author":"Ravichandran","year":"2019","journal-title":"Int. J. Electron. Telecommun."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Guo, S., Wang, K., Kang, H., Liu, T., Gao, Y., and Li, T. (2019). Bin loss for hard exudates segmentation in fundus images. Neurocomputing, In Press.","DOI":"10.1016\/j.neucom.2018.10.103"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.neucom.2018.02.035","article-title":"Exudate-based diabetic macular edema recognition in retinal images using cascaded deep residual networks","volume":"290","author":"Mo","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.cmpb.2016.09.018","article-title":"Detection of exudates in fundus photographs using deep neural networks and anatomical landmark detection fusion","volume":"137","year":"2016","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.ins.2017.08.050","article-title":"Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network","volume":"420","author":"Tan","year":"2017","journal-title":"Inf. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"101758","DOI":"10.1016\/j.artmed.2019.101758","article-title":"Ophthalmic diagnosis using deep learning with fundus images\u2014A critical review","volume":"102","author":"Sengupta","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.cmpb.2018.02.016","article-title":"Microaneurysm detection using fully convolutional neural networks","volume":"158","author":"Chudzik","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Phasuk, S., Poopresert, P., Yaemsuk, A., Suvannachart, P., Itthipanichpong, R., Chansangpetch, S., Manassakorn, A., Tantisevi, V., Rojanapongpun, P., and Tantibundhit, C. (2019, January 23\u201327). Automated glaucoma screening from retinal fundus image using deep learning. Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Berlin, Germany.","DOI":"10.1109\/EMBC.2019.8857136"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.ophtha.2019.09.036","article-title":"Deep learning approaches predict glaucomatous visual field damage from OCT optic nerve head en face images and retinal nerve fiber layer thickness maps","volume":"127","author":"Christopher","year":"2020","journal-title":"Ophthalmology"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"105341","DOI":"10.1016\/j.cmpb.2020.105341","article-title":"Offline computer-aided diagnosis for Glaucoma detection using fundus images targeted at mobile devices","volume":"192","author":"Martins","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_43","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_44","doi-asserted-by":"crossref","first-page":"2485","DOI":"10.1109\/TMI.2019.2899910","article-title":"Patch-based output space adversarial learning for joint optic disc and cup segmentation","volume":"38","author":"Wang","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"105320","DOI":"10.1016\/j.cmpb.2020.105320","article-title":"Deep learning algorithms for detection of diabetic retinopathy in retinal fundus photographs: A systematic review and meta-analysis","volume":"191","author":"Islam","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.neucom.2018.04.065","article-title":"Learning-based approach to segment pigment signs in fundus images for retinitis pigmentosa analysis","volume":"308","author":"Brancati","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Brancati, N., Frucci, M., Riccio, D., Di Perna, L., and Simonelli, F. (2019, January 9\u201313). Segmentation of pigment signs in fundus images for retinitis pigmentosa analysis by using deep learning. Proceedings of the Image Analysis and Processing, Trento, Italy.","DOI":"10.1007\/978-3-030-30645-8_40"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"128076","DOI":"10.1109\/ACCESS.2019.2939578","article-title":"Color filter array demosaicking using densely connected residual network","volume":"7","author":"Park","year":"2019","journal-title":"IEEE Access"},{"key":"ref_49","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_50","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_51","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_52","doi-asserted-by":"crossref","first-page":"112922","DOI":"10.1016\/j.eswa.2019.112922","article-title":"OR-Skip-Net: Outer residual skip network for skin segmentation in non-ideal situations","volume":"141","author":"Arsalan","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_53","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 Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2663","DOI":"10.1109\/TMI.2018.2845918","article-title":"H-DenseUNet: Hybrid densely connected UNet for liver and tumor segmentation from CT volumes","volume":"37","author":"Li","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., and Liang, J. (2018, January 20). UNet++: A Nested U-Net Architecture for Medical Image Segmentation. Proceedings of the Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Granada, Spain.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"ref_56","unstructured":"(2020, March 30). GeForce GTX TITAN X Graphics Processing Unit. Available online: https:\/\/www.geforce.com\/hardware\/desktop-gpus\/geforce-gtx-titan-x\/specifications."},{"key":"ref_57","unstructured":"(2020, March 23). MATLAB 2019b. Available online: https:\/\/ch.mathworks.com\/downloads\/web_downloads\/download_release?release=R2019b."},{"key":"ref_58","unstructured":"Kingma, D.P., and Ba, J.L. (2015, January 7\u20139). Adam: A method for stochastic optimization. Proceedings of the International Conference for Learning Representations, San Diego, CA, USA."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/12\/3454\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:40:34Z","timestamp":1760175634000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/12\/3454"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,18]]},"references-count":58,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["s20123454"],"URL":"https:\/\/doi.org\/10.3390\/s20123454","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2020,6,18]]}}}