{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T16:27:48Z","timestamp":1742920068854,"version":"3.40.3"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031210136"},{"type":"electronic","value":"9783031210143"}],"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.springernature.com\/gp\/researchers\/text-and-data-mining"},{"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.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-21014-3_19","type":"book-chapter","created":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T13:43:40Z","timestamp":1671111820000},"page":"181-190","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automatic Grading of Emphysema by Combining 3D Lung Tissue Appearance and Deformation Map Using a Two-Stream Fully Convolutional Neural Network"],"prefix":"10.1007","author":[{"given":"Mohammadreza","family":"Negahdar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,16]]},"reference":[{"key":"19_CR1","doi-asserted-by":"publisher","first-page":"155","DOI":"10.5005\/ijcdas-57-3-155","volume":"57","author":"L Fernandes","year":"2015","unstructured":"Fernandes, L., Gulati, N., Mesquita, A.M., Sardesai, M., Fernandes, Y.: Quantification of emphysema in chronic obstructive pulmonary disease by volumetric computed tomography of lung. Indian J. Chest Dis. Allied Sci. 57, 155\u2013160 (2015)","journal-title":"Indian J. Chest Dis. Allied Sci."},{"key":"19_CR2","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1097\/MCP.0b013e3282f3f18f","volume":"14","author":"H Omori","year":"2008","unstructured":"Omori, H., Fujimoto, K., Katoh, T.: Computed-tomography findings of emphysema: correlation with spirometric values. Curr. Opin. Pulm. Med. 14, 110\u2013114 (2008)","journal-title":"Curr. Opin. Pulm. Med."},{"issue":"2","key":"19_CR3","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1007\/s00330-015-3826-9","volume":"26","author":"MMW Wille","year":"2015","unstructured":"Wille, M.M.W., et al.: Visual assessment of early emphysema and interstitial abnormalities on CT is useful in lung cancer risk analysis. Eur. Radiol. 26(2), 487\u2013494 (2015). https:\/\/doi.org\/10.1007\/s00330-015-3826-9","journal-title":"Eur. Radiol."},{"key":"19_CR4","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1148\/radiol.2015141579","volume":"277","author":"DA Lynch","year":"2015","unstructured":"Lynch, D.A., Austin, J.H., Hogg, J.C., Grenier, P.A., Kauczor, H.U., et al.: CT-definable subtypes of chronic obstructive pulmonary disease: a statement of the fleischner society. Radiology 277, 192\u2013205 (2015)","journal-title":"Radiology"},{"key":"19_CR5","doi-asserted-by":"publisher","first-page":"639","DOI":"10.1097\/JTO.0000000000000144","volume":"9","author":"LA Hohberger","year":"2014","unstructured":"Hohberger, L.A., Schroeder, D.R., Bartholmai, B.J., Yang, P., Wendt, C.H., et al.: Correlation of regional emphysema and lung cancer: a lung tissue research consortium-based study. J. Thorac. Oncol. 9, 639\u2013645 (2014)","journal-title":"J. Thorac. Oncol."},{"key":"19_CR6","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1378\/chest.09-2836","volume":"138","author":"A Haruna","year":"2010","unstructured":"Haruna, A., Muro, S., Nakano, Y., Ohara, T., Hoshino, Y., et al.: CT scan findings of emphysema predict mortality in COPD. Chest 138, 635\u2013640 (2010)","journal-title":"Chest"},{"key":"19_CR7","doi-asserted-by":"publisher","first-page":"1207","DOI":"10.1109\/TMI.2016.2535865","volume":"35","author":"M Anthimopoulos","year":"2016","unstructured":"Anthimopoulos, M., Christodoulidis, S., Ebner, L., Christe, A., Mougiakakou, S.: Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans. Med. Imaging 35, 1207\u20131216 (2016)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"19_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/21681163.2015.1124249","volume":"6","author":"M Gao","year":"2018","unstructured":"Gao, M., Bagci, U., Lu, L., Wu, A., Buty, M., et al.: Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks. Comput. Methods Biomech. Biomed. Eng. Imaging Visual. 6, 1\u20136 (2018)","journal-title":"Comput. Methods Biomech. Biomed. Eng. Imaging Visual."},{"key":"19_CR9","doi-asserted-by":"crossref","unstructured":"\u00d8rting, S. N., Petersen, J., Thomsen, L. H., Wille, M. M. W., and Bruijne, M. d.: Detecting emphysema with multiple instance learning. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 510\u2013513. (2018)","DOI":"10.1109\/ISBI.2018.8363627"},{"key":"19_CR10","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1109\/JBHI.2016.2636929","volume":"21","author":"S Christodoulidis","year":"2017","unstructured":"Christodoulidis, S., Anthimopoulos, M., Ebner, L., Christe, A., Mougiakakou, S.: Multisource transfer learning with convolutional neural networks for lung pattern analysis. IEEE J. Biomed. Health Inform. 21, 76\u201384 (2017)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"19_CR11","doi-asserted-by":"crossref","unstructured":"Negahdar, M., Beymer, D.: Lung tissue characterization for emphysema differential diagnosis using deep convolutional neural networks. In: SPIE Medical Imaging, vol. 10950, pp. 109503R. San Diego, CA (2019)","DOI":"10.1117\/12.2513044"},{"key":"19_CR12","doi-asserted-by":"crossref","unstructured":"Bermejo-Pel\u00e1ez, D., Estepar, R.S.J., Ledesma-Carbayo, M.J.: Emphysema classification using a multi-view convolutional network. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 519\u2013522 (2018)","DOI":"10.1109\/ISBI.2018.8363629"},{"key":"19_CR13","doi-asserted-by":"crossref","unstructured":"Fischer, A.M., Varga-Szemes, A., van Assen, M., Griffith, L.P., Sahbaee, P. et al.: Comparison of artificial intelligence\u2013based fully automatic chest CT emphysema quantification to pulmonary function testing. Am. J. Roentgenol. 214(5), 1065\u20131071 (2020)","DOI":"10.2214\/AJR.19.21572"},{"key":"19_CR14","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1097\/RTI.0000000000000378","volume":"34","author":"H-U Kauczor","year":"2019","unstructured":"Kauczor, H.-U., Wielp\u00fctz, M.O., Jobst, B.J., Weinheimer, O., Gompelmann, D., et al.: Computed tomography imaging for novel therapies of chronic obstructive pulmonary disease. J. Thorac. Imaging 34, 202\u2013213 (2019)","journal-title":"J. Thorac. Imaging"},{"key":"19_CR15","doi-asserted-by":"crossref","unstructured":"Negahdar, M., Amini, A.A.: Regional lung strains via a volumetric mass conserving optical flow model. In: IEEE International Symposium on Biomedical Imaging (ISBI): From Nano to Macro, pp. 1475\u20131478. Barcelona, Spain (2012)","DOI":"10.1109\/ISBI.2012.6235850"},{"key":"19_CR16","doi-asserted-by":"crossref","unstructured":"Negahdar, M., Dunlap, N., Zacarias, A., Civelek, A.C., Woo, S.Y. et al.: Comparison of indices of regional lung function from 4-D X-ray CT: Jacobian vs. strain of deformation. In: IEEE International Symposium on Biomedical Imaging (ISBI): From Nano to Macro, pp. 648\u2013651. San Francisco, CA, USA (2013)","DOI":"10.1109\/ISBI.2013.6556558"},{"key":"19_CR17","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.radonc.2015.03.015","volume":"115","author":"M Negahdar","year":"2015","unstructured":"Negahdar, M., Fasola, C.E., Yu, A.S., von Eyben, R., Yamamoto, T., et al.: Noninvasive pulmonary nodule elastometry by CT and deformable image registration. Radiother. Oncol. 115, 35\u201340 (2015)","journal-title":"Radiother. Oncol."},{"key":"19_CR18","doi-asserted-by":"crossref","unstructured":"Joe Yue-Hei, N., Hausknecht, M., Vijayanarasimhan, S., Vinyals, O., Monga, R. et al.: Beyond short snippets: deep networks for video classification. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4694\u20134702 (2015)","DOI":"10.1109\/CVPR.2015.7299101"},{"key":"19_CR19","doi-asserted-by":"crossref","unstructured":"Wang, L., Ouyang, W., Wang, X., Lu, H.: Visual tracking with fully convolutional networks. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 3119\u20133127 (2015)","DOI":"10.1109\/ICCV.2015.357"},{"key":"19_CR20","doi-asserted-by":"crossref","unstructured":"Jain, S.D., Xiong, B., Grauman, K.: FusionSeg: learning to combine motion and appearance for fully automatic segmentation of generic objects in videos. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2117\u20132126 (2017)","DOI":"10.1109\/CVPR.2017.228"},{"key":"19_CR21","doi-asserted-by":"crossref","unstructured":"Li, S., Seybold, B., Vorobyov, A., Lei, X., Kuo, C.C.J.: Unsupervised video object segmentation with motion-based bilateral networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., et al. (eds.) Computer Vision \u2013 ECCV 2018, pp. 215-231. Cham (2018)","DOI":"10.1007\/978-3-030-01219-9_13"},{"key":"19_CR22","doi-asserted-by":"crossref","unstructured":"Gibson, E., Giganti, F., Hu, Y., Bonmati, E., Bandula, S., et al.: Towards image-guided pancreas and biliary endoscopy: automatic multi-organ segmentation on abdominal CT with dense dilated networks. In: Medical Image Computing and Computer Assisted Intervention \u2212 MICCAI 2017, pp. 728-736. Cham (2017)","DOI":"10.1007\/978-3-319-66182-7_83"},{"key":"19_CR23","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.-A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 3D Vision (3DV), 2016 Fourth International Conference on, pp. 565\u2013571 (2016)","DOI":"10.1109\/3DV.2016.79"},{"key":"19_CR24","doi-asserted-by":"crossref","unstructured":"Negahdar, M., Beymer, D., Syeda-Mahmood, T.: Automated volumetric lung segmentation of thoracic CT images using fully convolutional neural network. In: SPIE Medical Imaging, vol. 10575 (2018)","DOI":"10.1117\/12.2293723"},{"key":"19_CR25","doi-asserted-by":"publisher","first-page":"1822","DOI":"10.1109\/TMI.2018.2806309","volume":"37","author":"E Gibson","year":"2018","unstructured":"Gibson, E., Giganti, F., Hu, Y., Bonmati, E., Bandula, S., et al.: Automatic multi-organ segmentation on abdominal CT with dense V-networks. IEEE Trans. Med. Imaging 37, 1822\u20131834 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"19_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101558","volume":"58","author":"N Ghavami","year":"2019","unstructured":"Ghavami, N., Hu, Y., Gibson, E., Bonmati, E., Emberton, M., et al.: Automatic segmentation of prostate MRI using convolutional neural networks: investigating the impact of network architecture on the accuracy of volume measurement and MRI-ultrasound registration. Med. Image Anal. 58, 101558 (2019)","journal-title":"Med. Image Anal."},{"key":"19_CR27","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1109\/TMI.2009.2035616","volume":"29","author":"S Klein","year":"2010","unstructured":"Klein, S., Staring, M., Murphy, K., Viergever, M.A., Pluim, J.: elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imaging 29, 196\u2013205 (2010)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"19_CR28","doi-asserted-by":"crossref","unstructured":"Kalogeiton, V., Weinzaepfel, P., Ferrari, V., Schmid, C.: Action tubelet detector for spatio-temporal action localization. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 4415\u20134423 (2017)","DOI":"10.1109\/ICCV.2017.472"},{"key":"19_CR29","doi-asserted-by":"crossref","unstructured":"Singh, G., Saha, S., Sapienza, M., Torr, P., Cuzzolin, F.: Online real-time multiple spatiotemporal action localisation and prediction. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 3657\u20133666 (2017)","DOI":"10.1109\/ICCV.2017.393"},{"key":"19_CR30","doi-asserted-by":"crossref","unstructured":"Mohamed Hoesein, F.A., van Rikxoort, E., van Ginneken, B., de Jong, P.A., Prokop, M., et al.: Computed tomography-quantified emphysema distribution is associated with lung function decline. Eur. Respir. J. 40, 844\u2013850 (2012)","DOI":"10.1183\/09031936.00186311"},{"key":"19_CR31","doi-asserted-by":"publisher","first-page":"745","DOI":"10.1016\/j.mcna.2012.04.011","volume":"96","author":"KL Bailey","year":"2012","unstructured":"Bailey, K.L.: The importance of the assessment of pulmonary function in COPD. Med. Clin. North Am. 96, 745\u2013752 (2012)","journal-title":"Med. Clin. North Am."},{"issue":"11","key":"19_CR32","doi-asserted-by":"publisher","first-page":"2692","DOI":"10.1007\/s00330-014-3294-7","volume":"24","author":"MMW Wille","year":"2014","unstructured":"Wille, M.M.W., Thomsen, L.H., Dirksen, A., Petersen, J., Pedersen, J.H., Shaker, S.B.: Emphysema progression is visually detectable in low-dose CT in continuous but not in former smokers. Eur. Radiol. 24(11), 2692\u20132699 (2014). https:\/\/doi.org\/10.1007\/s00330-014-3294-7","journal-title":"Eur. Radiol."},{"key":"19_CR33","doi-asserted-by":"publisher","first-page":"2391","DOI":"10.1007\/s00330-009-1437-z","volume":"19","author":"CP Heussel","year":"2009","unstructured":"Heussel, C.P., Herth, F.J.F., Kappes, J., Hantusch, R., Hartlieb, S., et al.: Fully automatic quantitative assessment of emphysema in computed tomography: comparison with pulmonary function testing and normal values. Eur. Radiol. 19, 2391\u20132402 (2009)","journal-title":"Eur. Radiol."},{"key":"19_CR34","doi-asserted-by":"crossref","unstructured":"Bartholmai, B., Karwoski, R., Zavaletta, V., Robb, R., Holmes, D.: The lung tissue research consortium: an extensive open database containing histological, clinical, and radiological data to study chronic lung disease. In: The Insight Journal\u20142006 MICCAI Open Science Workshop (2006)","DOI":"10.54294\/hzdcno"}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-21014-3_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T13:47:19Z","timestamp":1671112039000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-21014-3_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031210136","9783031210143"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-21014-3_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"16 December 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MLMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","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":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlmi-med2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/mlmi2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"64","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"48","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"75% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}