{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T06:05:02Z","timestamp":1778825102714,"version":"3.51.4"},"reference-count":25,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2011,10,11]],"date-time":"2011-10-11T00:00:00Z","timestamp":1318291200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2015,9,1]],"date-time":"2015-09-01T00:00:00Z","timestamp":1441065600000},"content-version":"tdm","delay-in-days":1421,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["aip.org"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2011,11]]},"abstract":"<jats:sec><jats:title><jats:bold>Purpose:<\/jats:bold><\/jats:title><jats:p>It is challenging to reproducibly measure and compare cancer lesions on numerous follow\u2010up studies; the process is time\u2010consuming and error\u2010prone. In this paper, we show a method to automatically and reproducibly identify and segment abnormal lymph nodes in serial computed tomography (CT) exams.<\/jats:p><\/jats:sec><jats:sec><jats:title><jats:bold>Methods:<\/jats:bold><\/jats:title><jats:p>Our method leverages initial identification of enlarged (abnormal) lymph nodes in the baseline scan. We then identify an approximate region for the node in the follow\u2010up scans using nonrigid image registration. The baseline scan is also used to locate regions of normal, non\u2010nodal tissue surrounding the lymph node and to map them onto the follow\u2010up scans, in order to reduce the search space to locate the lymph node on the follow\u2010up scans. Adaptive region\u2010growing and clustering algorithms are then used to obtain the final contours for segmentation. We applied our method to 24 distinct enlarged lymph nodes at multiple time points from 14 patients. The scan at the earlier time point was used as the baseline scan to be used in evaluating the follow\u2010up scan, resulting in 70 total test cases (e.g., a series of scans obtained at 4 time points results in 3 test cases). For each of the 70 cases, a \u201creference standard\u201d was obtained by manual segmentation by a radiologist. Assessment according to response evaluation criteria in solid tumors (RECIST) using our method agreed with RECIST assessments made using the reference standard segmentations in all test cases, and by calculating node overlap ratio and Hausdorff distance between the computer and radiologist\u2010generated contours.<\/jats:p><\/jats:sec><jats:sec><jats:title><jats:bold>Results:<\/jats:bold><\/jats:title><jats:p>Compared to the reference standard, our method made the correct RECIST assessment for all 70 cases. The average overlap ratio was 80.7\u2009\u00b1\u20099.7% s.d., and the average Hausdorff distance was 3.2\u2009\u00b1\u20091.8 mm s.d. The concordance correlation between automated and manual segmentations was 0.978 (95% confidence interval 0.962, 0.984). The 100% agreement in our sample between our method and the standard with regard to RECIST classification suggests that the true disagreement rate is no more than 6%.<\/jats:p><\/jats:sec><jats:sec><jats:title><jats:bold>Conclusions:<\/jats:bold><\/jats:title><jats:p>Our automated lymph node segmentation method achieves excellent overall segmentation performance and provides equivalent RECIST assessment. It potentially will be useful to streamline and improve cancer lesion measurement and tracking and to improve assessment of cancer treatment response.<\/jats:p><\/jats:sec>","DOI":"10.1118\/1.3643027","type":"journal-article","created":{"date-parts":[[2011,10,11]],"date-time":"2011-10-11T22:03:12Z","timestamp":1318370592000},"page":"5879-5886","update-policy":"https:\/\/doi.org\/10.1063\/aip-crossmark-policy-page","source":"Crossref","is-referenced-by-count":16,"title":["Automated temporal tracking and segmentation of lymphoma on serial CT examinations"],"prefix":"10.1002","volume":"38","author":[{"given":"Jiajing","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hayit","family":"Greenspan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sandy","family":"Napel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel L.","family":"Rubin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2011,10,11]]},"reference":[{"key":"e_1_2_7_2_1","unstructured":"\u201cCancer Facts and Figures 2010 \u201d American Cancer Society (2010)."},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2009.2027117"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.3160107"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1097\/RTI.0b013e318173dd1f"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.3147146"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2413051887"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.3560877"},{"key":"e_1_2_7_9_1","doi-asserted-by":"crossref","unstructured":"C.Platero J. 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C.Tobar J.Sanguino G.Asensio andE.Santos \u201cLiver segmentation for hepatic lesions detection and characterisation \u201d inISBI 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro(Paris France 2008) pp.13\u201316.","DOI":"10.1109\/ISBI.2008.4540920"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-009-0359-1"},{"key":"e_1_2_7_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2008.11.014"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-88458-3_86"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbiomech.2007.07.016"},{"key":"e_1_2_7_14_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.2207133"},{"key":"e_1_2_7_15_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.2404617"},{"key":"e_1_2_7_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.1000236"},{"key":"e_1_2_7_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/42.796284"},{"key":"e_1_2_7_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2010.2053043"},{"key":"e_1_2_7_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.295913"},{"key":"e_1_2_7_20_1","volume-title":"Pattern Classification","author":"Duda R. 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