{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T08:52:34Z","timestamp":1772009554426,"version":"3.50.1"},"reference-count":35,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2016,5,1]],"date-time":"2016-05-01T00:00:00Z","timestamp":1462060800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"content-domain":{"domain":["cmpbjournal.com","clinicalkey.jp","clinicalkey.com","clinicalkey.es","clinicalkey.com.au","clinicalkey.fr","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Methods and Programs in Biomedicine"],"published-print":{"date-parts":[[2016,5]]},"DOI":"10.1016\/j.cmpb.2016.01.014","type":"journal-article","created":{"date-parts":[[2016,2,27]],"date-time":"2016-02-27T12:23:23Z","timestamp":1456575803000},"page":"75-85","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":25,"special_numbering":"C","title":["Active learning based segmentation of Crohns disease from abdominal MRI"],"prefix":"10.1016","volume":"128","author":[{"given":"Dwarikanath","family":"Mahapatra","sequence":"first","affiliation":[]},{"given":"Franciscus M.","family":"Vos","sequence":"additional","affiliation":[]},{"given":"Joachim M.","family":"Buhmann","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"12","key":"10.1016\/j.cmpb.2016.01.014_bib0005","doi-asserted-by":"crossref","first-page":"1439","DOI":"10.1097\/00042737-200112000-00007","article-title":"Increasing incidence of both juvenile-onset crohns disease and ulcerative colitis in scotland","volume":"13","author":"Armitage","year":"2001","journal-title":"Eur. J. Gastroenterol. Hepatol."},{"issue":"7","key":"10.1016\/j.cmpb.2016.01.014_bib0010","doi-asserted-by":"crossref","first-page":"983","DOI":"10.1136\/gut.30.7.983","article-title":"Development and validation of an endoscopic index of the severity for crohns disease: a prospective multicentre study","volume":"30","author":"Mary","year":"1989","journal-title":"Gut"},{"key":"10.1016\/j.cmpb.2016.01.014_bib0015","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1136\/gut.2008.167957","article-title":"Magnetic resonance for assessment of disease activity and severity in ileocolonic Crohn's disease","volume":"58","author":"Rimola","year":"2009","journal-title":"Gut"},{"key":"10.1016\/j.cmpb.2016.01.014_bib0020","series-title":"Proc. IEEE EMBC","first-page":"3974","article-title":"Computational modeling for assessment of IBD: to be or not to be","author":"Vos","year":"2012"},{"issue":"5","key":"10.1016\/j.cmpb.2016.01.014_bib0025","doi-asserted-by":"crossref","first-page":"920","DOI":"10.1007\/s10278-013-9576-9","article-title":"A supervised learning approach for crohn's disease detection using higher order image statistics and a novel shape asymmetry measure","volume":"26","author":"Mahapatra","year":"2013","journal-title":"J. Digit. Imaging"},{"issue":"1","key":"10.1016\/j.cmpb.2016.01.014_bib0030","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1148\/radiol.2471070611","article-title":"Inflammatory bowel disease diagnosed with US,MR, scinitigraphy, and CT meta-analysis of prospective studies","volume":"247","author":"Horsthuis","year":"2008","journal-title":"Radiology"},{"issue":"12","key":"10.1016\/j.cmpb.2016.01.014_bib0035","doi-asserted-by":"crossref","first-page":"2332","DOI":"10.1109\/TMI.2013.2282124","article-title":"Automatic detection and segmentation of crohn's disease tissues from abdominal MRI","volume":"32","author":"Mahapatra","year":"2013","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"4","key":"10.1016\/j.cmpb.2016.01.014_bib0040","doi-asserted-by":"crossref","first-page":"1504","DOI":"10.1109\/TIP.2014.2305073","article-title":"Analyzing training information from random forests for improved image segmentation","volume":"23","author":"Mahapatra","year":"2014","journal-title":"IEEE Trans. Image Proc."},{"key":"10.1016\/j.cmpb.2016.01.014_bib0045","series-title":"Empirical Methods in Natural Language Processing","first-page":"1070","article-title":"An analysis of active learning strategies for sequence labeling tasks","author":"Settles","year":"2008"},{"key":"10.1016\/j.cmpb.2016.01.014_bib0050","series-title":"Small Bowel Magnetic Resonance Imaging for Inflammatory Bowel Disease","first-page":"409","author":"Schunk","year":"2002"},{"issue":"3","key":"10.1016\/j.cmpb.2016.01.014_bib0055","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1007\/s00330-013-3015-7","article-title":"Evaluation of conventional, dynamic contrast enhanced and diffusion weighted MRI for quantitative crohn's disease assessment with histopathology of surgical specimens","volume":"24","author":"Tielbeek","year":"2014","journal-title":"Eur. Rad."},{"issue":"2","key":"10.1016\/j.cmpb.2016.01.014_bib0060","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1109\/TMI.2011.2168234","article-title":"Automatic detection and segmentation of lymph nodes from ct data","volume":"31","author":"Barbu","year":"2012","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"8","key":"10.1016\/j.cmpb.2016.01.014_bib0065","doi-asserted-by":"crossref","first-page":"1509","DOI":"10.1109\/TMI.2012.2190617","article-title":"Segmentation of skin lesions in 2-d and 3-d ultrasound images using a spatially coherent generalized Rayleigh mixture model","volume":"31","author":"Pereyra","year":"2012","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.cmpb.2016.01.014_bib0070","series-title":"Semi-Supervised Learning","author":"Chapelle","year":"2006"},{"key":"10.1016\/j.cmpb.2016.01.014_bib0075","series-title":"IPMI","first-page":"25","article-title":"Combining generative and discriminative models for semantic segmentation of ct scans via active learning","author":"Iglesias","year":"2011"},{"issue":"1","key":"10.1016\/j.cmpb.2016.01.014_bib0080","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2105-12-424","article-title":"An active learning based classification strategy for the minority class problem: application to histopathology annotation","volume":"12","author":"Doyle","year":"2011","journal-title":"BMC Bioinf."},{"issue":"10","key":"10.1016\/j.cmpb.2016.01.014_bib0085","doi-asserted-by":"crossref","first-page":"2314","DOI":"10.1016\/j.patcog.2011.01.007","article-title":"Segmentation of retinal blood vessels using the radial projection and semi-supervised approach","volume":"44","author":"You","year":"2011","journal-title":"Pattern Recog."},{"issue":"4","key":"10.1016\/j.cmpb.2016.01.014_bib0090","doi-asserted-by":"crossref","first-page":"1492","DOI":"10.1016\/j.eswa.2013.08.046","article-title":"Semi-supervised clustering for MR brain image segmentation","volume":"41","author":"Portela","year":"2014","journal-title":"Expert Syst. Appl."},{"issue":"6","key":"10.1016\/j.cmpb.2016.01.014_bib0095","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.neucom.2015.01.020","article-title":"Posterior Distribution Learning (PDL): a novel supervised learning framework using unlabeled samples to improve classification performance","volume":"157","author":"Tu","year":"2015","journal-title":"Neurocomputing"},{"key":"10.1016\/j.cmpb.2016.01.014_bib0100","series-title":"IEEE ICCV Workshops","first-page":"1393","article-title":"On-line random forests","author":"Saffari","year":"2009"},{"key":"10.1016\/j.cmpb.2016.01.014_bib0105","series-title":"Proc. MICCAI, Part 2","first-page":"214","article-title":"Semi-supervised and active learning for automatic segmentation of crohn's disease","author":"Mahapatra","year":"2013"},{"key":"10.1016\/j.cmpb.2016.01.014_bib0110","series-title":"Proc. IEEE ISBI","first-page":"226","article-title":"Active learning based segmentation of crohn's disease using principles of visual saliency","author":"Mahapatra","year":"2014"},{"issue":"4","key":"10.1016\/j.cmpb.2016.01.014_bib0115","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1002\/1097-0193(200008)10:4<204::AID-HBM60>3.0.CO;2-2","article-title":"Rapid and effective correction of RF inhomogeneity for high field magnetic resonance imaging","volume":"10","author":"Cohen","year":"2000","journal-title":"Hum. Brain Map."},{"issue":"7","key":"10.1016\/j.cmpb.2016.01.014_bib0120","doi-asserted-by":"crossref","first-page":"2007","DOI":"10.1109\/TIP.2011.2146190","article-title":"A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI","volume":"20","author":"Li","year":"2011","journal-title":"IEEE Trans. Image Proc."},{"issue":"3","key":"10.1016\/j.cmpb.2016.01.014_bib0125","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1109\/LSP.2014.2346538","article-title":"Region based segmentation in the presence of intensity inhomogeneity using Legendre polynomials","volume":"22","author":"Mukherjee","year":"2015","journal-title":"IEEE Signal Proc. Lett."},{"issue":"3","key":"10.1016\/j.cmpb.2016.01.014_bib0130","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1109\/TBME.2013.2289306","article-title":"Prostate MRI segmentation using learned semantic knowledge and graph cuts","volume":"61","author":"Mahapatra","year":"2014","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/j.cmpb.2016.01.014_bib0135","series-title":"Proc. SPIE Medical Imaging","doi-asserted-by":"crossref","DOI":"10.1117\/12.2006698","article-title":"Localizing and segmenting crohn's disease affected regions in abdominal MRI using novel context features","author":"Mahapatra","year":"2013"},{"issue":"10","key":"10.1016\/j.cmpb.2016.01.014_bib0140","doi-asserted-by":"crossref","first-page":"3020","DOI":"10.1109\/TIP.2006.877516","article-title":"Three-dimensional nonlinear invisible boundary detection","volume":"15","author":"Petrou","year":"2006","journal-title":"IEEE Trans. Image Proc."},{"key":"10.1016\/j.cmpb.2016.01.014_bib0150","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.neuroimage.2014.12.042","article-title":"LINKS: Learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images","volume":"108","author":"Wang","year":"2015","journal-title":"NeuroImage"},{"issue":"1","key":"10.1016\/j.cmpb.2016.01.014_bib0155","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"10.1016\/j.cmpb.2016.01.014_bib0160","series-title":"Decision Forests for Computer Vision and Medical Image Analysis","author":"Criminisi","year":"2013"},{"key":"10.1016\/j.cmpb.2016.01.014_bib0165","doi-asserted-by":"crossref","first-page":"1222","DOI":"10.1109\/34.969114","article-title":"Fast approximate energy minimization via graph cuts","volume":"23","author":"Boykov","year":"2001","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"3","key":"10.1016\/j.cmpb.2016.01.014_bib0170","first-page":"18","article-title":"Classification and regression by random forest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"issue":"2","key":"10.1016\/j.cmpb.2016.01.014_bib0175","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1023\/A:1007330508534","article-title":"Selective sampling using the query by committee algorithm","volume":"28","author":"Freund","year":"1997","journal-title":"Mach. Learn."},{"key":"10.1016\/j.cmpb.2016.01.014_bib0180","series-title":"An Introduction to Conditional Random Fields for Relational Learning","author":"Sutton","year":"2006"}],"container-title":["Computer Methods and Programs in Biomedicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260716000067?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260716000067?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2018,9,14]],"date-time":"2018-09-14T20:57:22Z","timestamp":1536958642000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0169260716000067"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,5]]},"references-count":35,"alternative-id":["S0169260716000067"],"URL":"https:\/\/doi.org\/10.1016\/j.cmpb.2016.01.014","relation":{},"ISSN":["0169-2607"],"issn-type":[{"value":"0169-2607","type":"print"}],"subject":[],"published":{"date-parts":[[2016,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Active learning based segmentation of Crohns disease from abdominal MRI","name":"articletitle","label":"Article Title"},{"value":"Computer Methods and Programs in Biomedicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cmpb.2016.01.014","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"Copyright \u00a9 2016 Elsevier Ireland Ltd. All rights reserved.","name":"copyright","label":"Copyright"}]}}