{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T06:04:43Z","timestamp":1725861883484},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319420158"},{"type":"electronic","value":"9783319420165"}],"license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016]]},"DOI":"10.1007\/978-3-319-42016-5_7","type":"book-chapter","created":{"date-parts":[[2016,7,29]],"date-time":"2016-07-29T12:52:55Z","timestamp":1469796775000},"page":"72-82","source":"Crossref","is-referenced-by-count":4,"title":["Automated Segmentation of CBCT Image with Prior-Guided Sequential Random Forest"],"prefix":"10.1007","author":[{"given":"Li","family":"Wang","sequence":"first","affiliation":[]},{"given":"Yaozong","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Feng","family":"Shi","sequence":"additional","affiliation":[]},{"given":"Gang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Ken-Chung","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Zhen","family":"Tang","sequence":"additional","affiliation":[]},{"given":"James J.","family":"Xia","sequence":"additional","affiliation":[]},{"given":"Dinggang","family":"Shen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2016,7,30]]},"reference":[{"key":"7_CR1","doi-asserted-by":"crossref","first-page":"5809","DOI":"10.1118\/1.4929974","volume":"42","author":"L Wang","year":"2015","unstructured":"Wang, L., Ren, Y., Gao, Y., Tang, Z., Chen, K.C., Li, J., Shen, S.G., Yan, J., Lee, P.K., Chow, B., Xia, J.J., Shen, D.: Estimating patient-specific and anatomically correct reference model for craniomaxillofacial deformity via sparse representation. Med. Phys. 42, 5809 (2015)","journal-title":"Med. Phys."},{"key":"7_CR2","doi-asserted-by":"crossref","first-page":"043503","DOI":"10.1118\/1.4868455","volume":"41","author":"L Wang","year":"2014","unstructured":"Wang, L., Chen, K.C., Gao, Y., Shi, F., Liao, S., Li, G., Shen, S.G.F., Yan, J., Lee, P.K.M., Chow, B., Liu, N.X., Xia, J.J., Shen, D.: Automated bone segmentation from dental CBCT images using patch-based sparse representation and convex optimization. Med. Phys. 41, 043503 (2014)","journal-title":"Med. Phys."},{"key":"7_CR3","first-page":"968","volume-title":"MICCAI 2009","author":"BH Le","year":"2009","unstructured":"Le, B.H., Deng, Z., Xia, J., Chang, Y.-B., Zhou, X.: An interactive geometric technique for upper and lower teeth segmentation. In: Yang, G.-Z., et al. (eds.) MICCAI 2009, vol. 5762, pp. 968\u2013975. Springer, Berlin Heidelberg (2009)"},{"key":"7_CR4","unstructured":"Hassan, B.A.: Applications of Cone Beam Computed Tomography in Orthodontics and Endodontics. Thesis, Reading University, VU University Amsterdam (2010)"},{"key":"7_CR5","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1016\/j.jvcir.2010.02.009","volume":"21","author":"L He","year":"2010","unstructured":"He, L., Zheng, S.F., Wang, L.: Integrating local distribution information with level set for boundary extraction. J. Vis. Commun. Image Represent. 21, 343\u2013354 (2010)","journal-title":"J. Vis. Commun. Image Represent."},{"key":"7_CR6","first-page":"76","volume-title":"MICCAI 2009","author":"D Kainmueller","year":"2009","unstructured":"Kainmueller, D., Lamecker, H., Seim, H., Zinser, M., Zachow, S.: Automatic extraction of mandibular nerve and bone from cone-beam CT data. In: Yang, G.-Z., et al. (eds.) MICCAI 2009, vol. 5762, pp. 76\u201383. Springer, Heidelberg (2009)"},{"key":"7_CR7","doi-asserted-by":"crossref","unstructured":"Gollmer, S.T., Buzug, T.M.: Fully automatic shape constrained mandible segmentation from cone-beam CT data. In: ISBI, pp. 1272\u20131275 (2012)","DOI":"10.1109\/ISBI.2012.6235794"},{"key":"7_CR8","first-page":"451","volume-title":"MICCAI 2011","author":"S Zhang","year":"2011","unstructured":"Zhang, S., Zhan, Y., Dewan, M., Huang, J., Metaxas, D.N., Zhou, X.S.: Deformable segmentation via sparse shape representation. In: Fichtinger, G., et al. (eds.) MICCAI 2011, vol. 6892, pp. 451\u2013458. Springer, Heidelberg (2011)"},{"key":"7_CR9","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.media.2011.08.004","volume":"16","author":"S Zhang","year":"2012","unstructured":"Zhang, S., Zhan, Y., Dewan, M., Huang, J., Metaxas, D.N., Zhou, X.S.: Towards robust and effective shape modeling: sparse shape composition. Med. Image Anal. 16, 265\u2013277 (2012)","journal-title":"Med. Image Anal."},{"key":"7_CR10","doi-asserted-by":"crossref","first-page":"1385","DOI":"10.1016\/j.media.2012.07.007","volume":"16","author":"ST Zhang","year":"2012","unstructured":"Zhang, S.T., Zhan, Y.Q., Metaxas, D.N.: Deformable segmentation via sparse representation and dictionary learning. Med. Image Anal. 16, 1385\u20131396 (2012)","journal-title":"Med. Image Anal."},{"key":"7_CR11","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.neuroimage.2013.11.040","volume":"89","author":"L Wang","year":"2014","unstructured":"Wang, L., Shi, F., Gao, Y., Li, G., Gilmore, J.H., Lin, W., Shen, D.: Integration of sparse multi-modality representation and anatomical constraint for isointense infant brain MR image segmentation. NeuroImage 89, 152\u2013164 (2014)","journal-title":"NeuroImage"},{"key":"7_CR12","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.neuroimage.2013.08.008","volume":"84","author":"L Wang","year":"2014","unstructured":"Wang, L., Shi, F., Li, G., Gao, Y., Lin, W., Gilmore, J.H., Shen, D.: Segmentation of neonatal brain MR images using patch-driven level sets. NeuroImage 84, 141\u2013158 (2014)","journal-title":"NeuroImage"},{"key":"7_CR13","doi-asserted-by":"crossref","first-page":"4663","DOI":"10.1002\/hbm.22502","volume":"35","author":"F Shi","year":"2014","unstructured":"Shi, F., Wang, L., Wu, G.R., Li, G., Gilmore, J.H., Lin, W.L., Shen, D.: Neonatal atlas construction using sparse representation. Hum. Brain Mapp. 35, 4663\u20134677 (2014)","journal-title":"Hum. Brain Mapp."},{"key":"7_CR14","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45, 5\u201332 (2001)","journal-title":"Mach. Learn."},{"key":"7_CR15","doi-asserted-by":"crossref","first-page":"1262","DOI":"10.1016\/j.media.2014.06.010","volume":"18","author":"D Zikic","year":"2014","unstructured":"Zikic, D., Glocker, B., Criminisi, A.: Encoding atlases by randomized classification forests for efficient multi-atlas label propagation. Med. Image Anal. 18, 1262\u20131273 (2014)","journal-title":"Med. Image Anal."},{"key":"7_CR16","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.neuroimage.2014.12.042","volume":"108","author":"L Wang","year":"2015","unstructured":"Wang, L., Gao, Y., Shi, F., Li, G., Gilmore, J.H., Lin, W., Shen, D.: LINKS: learning-based multi-source integration framework for segmentation of infant brain images. NeuroImage 108, 160\u2013172 (2015)","journal-title":"NeuroImage"},{"key":"7_CR17","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1007\/978-3-642-33454-2_46","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2012","author":"D Zikic","year":"2012","unstructured":"Zikic, D., Glocker, B., et al.: Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel MR. In: Ayache, N., et al. (eds.) Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2012, vol. 7512, pp. 369\u2013376. Springer, Heidelberg (2012)"},{"key":"7_CR18","doi-asserted-by":"crossref","first-page":"1744","DOI":"10.1109\/TPAMI.2009.186","volume":"32","author":"Z Tu","year":"2010","unstructured":"Tu, Z., Bai, X.: Auto-context and its application to high-level vision tasks and 3D brain image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1744\u20131757 (2010)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"7_CR19","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1023\/B:VISI.0000013087.49260.fb","volume":"57","author":"P Viola","year":"2004","unstructured":"Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57, 137\u2013154 (2004)","journal-title":"Int. J. Comput. Vis."},{"key":"7_CR20","first-page":"693","volume":"8","author":"C Sutton","year":"2007","unstructured":"Sutton, C., McCallum, A., Rohanimanesh, K.: Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data. J. Mach. Learn. Res. 8, 693\u2013723 (2007)","journal-title":"J. Mach. Learn. Res."},{"key":"7_CR21","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1016\/j.tics.2007.09.009","volume":"11","author":"A Oliva","year":"2007","unstructured":"Oliva, A., Torralba, A.: The role of context in object recognition. Trends Cogn. Sci. 11, 520\u2013527 (2007)","journal-title":"Trends Cogn. Sci."},{"key":"7_CR22","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1109\/34.993558","volume":"24","author":"S Belongie","year":"2002","unstructured":"Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24, 509\u2013522 (2002)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"7_CR23","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1109\/TPAMI.1984.4767596","volume":"6","author":"S Geman","year":"1984","unstructured":"Geman, S., Geman, D.: Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 6, 721\u2013741 (1984)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"7_CR24","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1109\/72.991427","volume":"13","author":"C-W Hsu","year":"2002","unstructured":"Hsu, C.-W., Lin, C.-J.: A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Netw. 13, 415\u2013425 (2002)","journal-title":"IEEE Trans. Neural Netw."},{"key":"7_CR25","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1007\/978-3-642-40760-4_9","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2013","author":"D Zikic","year":"2013","unstructured":"Zikic, D., Glocker, B., Criminisi, A.: Atlas encoding by randomized forests for efficient label propagation. In: Mori, K., et al. (eds.) Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2013, vol. 8151, pp. 66\u201373. Springer, Berlin Heidelberg (2013)"},{"key":"7_CR26","doi-asserted-by":"crossref","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"}],"container-title":["Lecture Notes in Computer Science","Medical Computer Vision: Algorithms for Big Data"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-42016-5_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2017,6,24]],"date-time":"2017-06-24T19:30:31Z","timestamp":1498332631000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-42016-5_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"ISBN":["9783319420158","9783319420165"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-42016-5_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2016]]}}}