{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T12:32:06Z","timestamp":1787920326359,"version":"build-2784847793"},"reference-count":24,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2018,10,24]],"date-time":"2018-10-24T00:00:00Z","timestamp":1540339200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000062","name":"National Institute of Diabetes and Digestive and Kidney Diseases","doi-asserted-by":"publisher","award":["R01DK100404"],"award-info":[{"award-number":["R01DK100404"]}],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging"],"published-print":{"date-parts":[[2019,6]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Contrast\u2010enhanced MRI of the small bowel is an effective imaging sequence for the detection and characterization of disease burden in pediatric Crohn's disease (CD). However, visualization and quantification of disease burden requires scrolling back and forth through 3D images to follow the anatomy of the bowel, and it can be difficult to fully appreciate the extent of disease.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To develop and evaluate a method that offers better visualization and quantitative assessment of CD from MRI.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Study Type<\/jats:title>\n                    <jats:p>Retrospective.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Population<\/jats:title>\n                    <jats:p>Twenty\u2010three pediatric patients with CD.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Field Strength\/Sequence<\/jats:title>\n                    <jats:p>\n                      1.5T MRI system and T\n                      <jats:sub>1<\/jats:sub>\n                      \u2010weighted postcontrast VIBE sequence.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Assessment<\/jats:title>\n                    <jats:p>The convolutional neural network (CNN) segmentation of the bowel's lumen, wall, and background was compared with manual boundary delineation. We assessed the reproducibility and the capability of the extracted markers to differentiate between different levels of disease defined after a consensus review by two experienced radiologists.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Statistical Tests<\/jats:title>\n                    <jats:p>\n                      The segmentation algorithm was assessed using the Dice similarity coefficient (DSC) and boundary distances between the CNN and manual boundary delineations. The capability of the extracted markers to differentiate between different disease levels was determined using a\n                      <jats:italic>t<\/jats:italic>\n                      \u2010test. The reproducibility of the extracted markers was assessed using the mean relative difference (MRD), Pearson correlation, and Bland\u2013Altman analysis.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Our CNN exhibited DSCs of 75\u2009\u00b1\u200918%, 81\u2009\u00b1\u20098%, and 97\u2009\u00b1\u20092% for the lumen, wall, and background, respectively. The extracted markers of wall thickness at the location of min radius (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.0013) and the median value of relative contrast enhancement (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009=\u20090.0033) could differentiate active and nonactive disease segments. Other extracted markers could differentiate between segments with strictures and segments without strictures (\n                      <jats:italic>P<\/jats:italic>\n                      &lt; 0.05). The observers' agreement in measuring stricture length was &gt;3 times superior when computed on curved planar reformatting images compared with the conventional scheme.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Data Conclusion<\/jats:title>\n                    <jats:p>The results of this study show that the newly developed method is efficient for visualization and assessment of CD.<\/jats:p>\n                    <jats:p>\n                      <jats:bold>Level of Evidence:<\/jats:bold>\n                      4\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>Technical Efficacy:<\/jats:bold>\n                      Stage 2\n                    <\/jats:p>\n                    <jats:p>J. Magn. Reson. Imaging 2019;49:1565\u20131576.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jmri.26330","type":"journal-article","created":{"date-parts":[[2018,10,24]],"date-time":"2018-10-24T10:03:07Z","timestamp":1540375387000},"page":"1565-1576","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Curved planar reformatting and convolutional neural network\u2010based segmentation of the small bowel for visualization and quantitative assessment of pediatric Crohn's disease from MRI"],"prefix":"10.1002","volume":"49","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2619-597X","authenticated-orcid":false,"given":"Yechiel","family":"Lamash","sequence":"first","affiliation":[{"name":"Computational Radiology Laboratory, Department of Radiology, Children's Hospital Boston Harvard Medical School Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sila","family":"Kurugol","sequence":"additional","affiliation":[{"name":"Computational Radiology Laboratory, Department of Radiology, Children's Hospital Boston Harvard Medical School Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Moti","family":"Freiman","sequence":"additional","affiliation":[{"name":"Computational Radiology Laboratory, Department of Radiology, Children's Hospital Boston Harvard Medical School Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeannette M.","family":"Perez\u2010Rossello","sequence":"additional","affiliation":[{"name":"Department of Radiology Children's Hospital Boston, Harvard Medical School Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael J.","family":"Callahan","sequence":"additional","affiliation":[{"name":"Department of Radiology Children's Hospital Boston, Harvard Medical School Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Athos","family":"Bousvaros","sequence":"additional","affiliation":[{"name":"Center for Inflammatory Bowel Disease Treatment and Research Children's Hospital Boston, Harvard Medical School Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simon K.","family":"Warfield","sequence":"additional","affiliation":[{"name":"Computational Radiology Laboratory, Department of Radiology, Children's Hospital Boston Harvard Medical School Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2018,10,24]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.1053\/j.gastro.2017.11.274"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.1136\/gut.2008.167957"},{"key":"e_1_2_6_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33612-6_11"},{"key":"e_1_2_6_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2013.2282124"},{"key":"e_1_2_6_6_1","doi-asserted-by":"crossref","unstructured":"MahapatraD Sch\u00fcfflerPJ TielbeekJA et al. eds.Active learning based segmentation of Crohn's disease using principles of visual saliency. 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