{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,19]],"date-time":"2026-04-19T01:14:05Z","timestamp":1776561245217,"version":"3.51.2"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031120527","type":"print"},{"value":"9783031120534","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-12053-4_26","type":"book-chapter","created":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T09:15:50Z","timestamp":1658740550000},"page":"339-356","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Lung Segmentation Using ResUnet++ Powered by Variational Auto Encoder-Based Enhancement in Chest X-ray Images"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3060-5986","authenticated-orcid":false,"given":"Samar","family":"Ibrahim","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2807-6317","authenticated-orcid":false,"given":"Kareem","family":"Elgohary","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mahmoud","family":"Higazy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thanaa","family":"Mohannad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9886-1364","authenticated-orcid":false,"given":"Sahar","family":"Selim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7936-3522","authenticated-orcid":false,"given":"Mustafa","family":"Elattar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,7,25]]},"reference":[{"key":"26_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/J.BSPC.2021.103398","volume":"73","author":"A Maity","year":"2022","unstructured":"Maity, A., Nair, T.R., Mehta, S., Prakasam, P.: Automatic lung parenchyma segmentation using a deep convolutional neural network from chest X-rays. Biomed. Signal Process. Control 73, 103398 (2022). https:\/\/doi.org\/10.1016\/J.BSPC.2021.103398","journal-title":"Biomed. Signal Process. Control"},{"key":"26_CR2","unstructured":"Dixon, S.: Diagnostic Imaging Dataset Statistical Release (2022)"},{"key":"26_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/J.IMU.2020.100391","volume":"20","author":"S Bharati","year":"2020","unstructured":"Bharati, S., Podder, P., Mondal, M.R.H.: Hybrid deep learning for detecting lung diseases from X-ray images. Inform. Med. Unlocked 20, 100391 (2020). https:\/\/doi.org\/10.1016\/J.IMU.2020.100391","journal-title":"Inform. Med. Unlocked"},{"issue":"1","key":"26_CR4","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1006\/CVIU.1995.1004","volume":"61","author":"TF Cootes","year":"1995","unstructured":"Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models-their training and application. Comput. Vis. Image Underst. 61(1), 38\u201359 (1995). https:\/\/doi.org\/10.1006\/CVIU.1995.1004","journal-title":"Comput. Vis. Image Underst."},{"issue":"1","key":"26_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/BF01385685","volume":"66","author":"V Caselles","year":"1993","unstructured":"Caselles, V., Catt\u00e9, F., Coll, T., Dibos, F.: A geometric model for active contours in image processing. Numer. Math. 66(1), 1\u201331 (1993). https:\/\/doi.org\/10.1007\/BF01385685","journal-title":"Numer. Math."},{"issue":"3","key":"26_CR6","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1007\/s11277-018-5777-3","volume":"101","author":"R Hooda","year":"2018","unstructured":"Hooda, R., Mittal, A., Sofat, S.: An efficient variant of fully-convolutional network for segmenting lung fields from chest radiographs. Wirel. Pers. Commun. 101(3), 1559\u20131579 (2018). https:\/\/doi.org\/10.1007\/s11277-018-5777-3","journal-title":"Wirel. Pers. Commun."},{"issue":"2","key":"26_CR7","doi-asserted-by":"publisher","first-page":"369","DOI":"10.3390\/S21020369","volume":"21","author":"M Kim","year":"2021","unstructured":"Kim, M., Lee, B.D.: Automatic lung segmentation on chest x-rays using self-attention deep neural network. Sensors 21(2), 369 (2021). https:\/\/doi.org\/10.3390\/S21020369","journal-title":"Sensors"},{"key":"26_CR8","doi-asserted-by":"publisher","unstructured":"Souza, J.C., Bandeira Diniz, J.O., Ferreira, J.L., Fran\u00e7a da Silva, G.L., Corr\u00eaa Silva, A., de Paiva, A.C.: An automatic method for lung segmentation and reconstruction in chest X-ray using deep neural networks. Comput. Methods Programs Biomed. 177, 285\u2013296 (2019). https:\/\/doi.org\/10.1016\/J.CMPB.2019.06.005","DOI":"10.1016\/J.CMPB.2019.06.005"},{"issue":"12","key":"26_CR9","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481\u20132495 (2017). https:\/\/doi.org\/10.1109\/TPAMI.2016.2644615","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"26_CR10","unstructured":"Chest X-Ray Images Generation Using GAN | College of Science and Engineering. https:\/\/cse.umn.edu\/datascience\/chest-x-ray-images-generation-using-gan"},{"key":"26_CR11","doi-asserted-by":"publisher","unstructured":"Bell, D., Shetty, A.: X-ray artifacts. Radiopaedia.org, January 2014. https:\/\/doi.org\/10.53347\/RID-27307","DOI":"10.53347\/RID-27307"},{"key":"26_CR12","unstructured":"JSRT Database | Japanese Society of Radiological Technology. http:\/\/db.jsrt.or.jp\/eng.php"},{"key":"26_CR13","unstructured":"Tuberculosis Chest X-ray Image Data Sets. - LHNCBC Abstract. https:\/\/lhncbc.nlm.nih.gov\/LHCpublications\/pubs\/TuberculosisChestXrayImageDataSets.html"},{"key":"26_CR14","doi-asserted-by":"publisher","unstructured":"Jha, D., et al.: ResUNet++: an advanced architecture for medical image segmentation. In: Proceedings of 2019 IEEE International Symposium on Multimedia, ISM 2019, pp. 225\u2013230, November 2019. https:\/\/doi.org\/10.48550\/arxiv.1911.07067","DOI":"10.48550\/arxiv.1911.07067"},{"key":"26_CR15","unstructured":"Selvan, R., et al.: Lung Segmentation from Chest X-rays using Variational Data Imputation, May 2020. http:\/\/arxiv.org\/abs\/2005.10052"},{"key":"26_CR16","unstructured":"COVID-19 Radiography Database | Kaggle. https:\/\/www.kaggle.com\/datasets\/tawsifurrahman\/covid19-radiography-database"},{"key":"26_CR17","doi-asserted-by":"crossref","unstructured":"Chen, B., Zhang, Z., Lin, J., Chen, Y., Lu, G.: Pattern recognition letters two-stream collaborative network for multi-label chest X-ray image classification with lung segmentation (2020)","DOI":"10.1016\/j.patrec.2020.04.016"},{"issue":"18","key":"26_CR18","doi-asserted-by":"publisher","first-page":"6264","DOI":"10.3390\/APP10186264","volume":"10","author":"V Bosdelekidis","year":"2020","unstructured":"Bosdelekidis, V., Ioakeimidis, N.S.: Lung field segmentation in chest X-rays: a deformation-tolerant procedure based on the approximation of rib cage seed points. Appl. Sci. 10(18), 6264 (2020). https:\/\/doi.org\/10.3390\/APP10186264","journal-title":"Appl. Sci."},{"issue":"1","key":"26_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/S12880-020-00514-Y\/TABLES\/6","volume":"20","author":"N Reamaroon","year":"2020","unstructured":"Reamaroon, N., et al.: Robust segmentation of lung in chest X-ray: applications in analysis of acute respiratory distress syndrome. BMC Med. Imaging 20(1), 1\u201313 (2020). https:\/\/doi.org\/10.1186\/S12880-020-00514-Y\/TABLES\/6","journal-title":"BMC Med. Imaging"},{"key":"26_CR20","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-38752-5_1","volume-title":"Applied Computing to Support Industry: Innovation and Technology","author":"YA Hamad","year":"2020","unstructured":"Hamad, Y.A., Simonov, K., Naeem, M.B.: Lung boundary detection and classification in chest X-rays images based on neural network. In: Khalaf, M.I., Al-Jumeily, D., Lisitsa, A. (eds.) ACRIT 2019. CCIS, vol. 1174, pp. 3\u201316. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-38752-5_1"},{"issue":"8","key":"26_CR21","doi-asserted-by":"publisher","first-page":"1865","DOI":"10.1109\/TMI.2018.2806086","volume":"37","author":"AA Novikov","year":"2018","unstructured":"Novikov, A.A., Lenis, D., Major, D., Hladuvka, J., Wimmer, M., Buhler, K.: Fully convolutional architectures for multiclass segmentation in chest radiographs. IEEE Trans. Med. Imaging 37(8), 1865\u20131876 (2018). https:\/\/doi.org\/10.1109\/TMI.2018.2806086","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"4","key":"26_CR22","doi-asserted-by":"publisher","first-page":"545","DOI":"10.3390\/MATH8040545","volume":"8","author":"HJ Chen","year":"2020","unstructured":"Chen, H.J., Ruan, S.J., Huang, S.W., Peng, Y.T.: Lung X-ray segmentation using deep convolutional neural networks on contrast-enhanced binarized images. Mathematics 8(4), 545 (2020). https:\/\/doi.org\/10.3390\/MATH8040545","journal-title":"Mathematics"},{"key":"26_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1007\/978-3-030-00889-5_30","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"W Dai","year":"2018","unstructured":"Dai, W., Dong, N., Wang, Z., Liang, X., Zhang, H., Xing, E.P.: SCAN: structure correcting adversarial network for organ segmentation in chest X-rays. In: Stoyanov, D., et al. (eds.) DLMIA\/ML-CDS 2018. LNCS, vol. 11045, pp. 263\u2013273. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00889-5_30"}],"container-title":["Lecture Notes in Computer Science","Medical Image Understanding and Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-12053-4_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,8]],"date-time":"2024-02-08T08:06:59Z","timestamp":1707379619000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-12053-4_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031120527","9783031120534"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-12053-4_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"25 July 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MIUA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Annual Conference on Medical Image Understanding and Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cambridge","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 July 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 July 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miua2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miua2022.com\/home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}