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Ground truth contours were generated using a simultaneous truth and performance level estimation (STAPLE) algorithm. Twenty total autosegmentation methods were evaluated in ADMIRE: 1\u20139) atlas\u2010based autosegmentation using a population atlas library (PAL) of 5\/10\/15 patients with STAPLE, patch fusion (PF), random forest (RF) for label fusion; 10\u201319) autosegmentation using images from a patient's 1\u20134 prior fractions (individualized patient prior [IPP]) using STAPLE\/PF\/RF; 20) deep learning (DL) (3D ResUNet trained on 43 ground truth structure sets plus 45 contoured by one observer). Execution time was measured for each method. Autosegmented structures were compared to ground truth structures using the Dice similarity coefficient, mean surface distance (MSD), Hausdorff distance (HD), and Jaccard index (JI). For each metric and OAR, performance was compared to the inter\u2010observer variability using Dunn's test with control. Methods were compared pairwise using the Steel\u2010Dwass test for each metric pooled across all OARs. Further dosimetric analysis was performed on three high\u2010performing autosegmentation methods (DL, IPP with RF and 4 fractions [IPP_RF_4], IPP with 1 fraction [IPP_1]), and one low\u2010performing (PAL with STAPLE and 5 atlases [PAL_ST_5]). For five patients, delivered doses from clinical plans were recalculated on setup images with ground truth and autosegmented structure sets. Differences in maximum and mean dose to each structure between the ground truth and autosegmented structures were calculated and correlated with geometric metrics.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>DL and IPP methods performed best overall, all significantly outperforming inter\u2010observer variability and with no significant difference between methods in pairwise comparison. PAL methods performed worst overall; most were not significantly different from the inter\u2010observer variability or from each other. DL was the fastest method (33\u00a0s per case) and PAL methods the slowest (3.7\u201313.8 min per case). Execution time increased with a number of prior fractions\/atlases for IPP and PAL. For DL, IPP_1, and IPP_RF_4, the majority (95%) of dose differences were within\u00a0\u00b1\u00a0250\u00a0cGy from ground truth, but outlier differences up to 785\u00a0cGy occurred. Dose differences were much higher for PAL_ST_5, with outlier differences up to 1920\u00a0cGy. Dose differences showed weak but significant correlations with all geometric metrics (<jats:italic>R<\/jats:italic>2 between 0.030 and 0.314).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The autosegmentation methods offering the best combination of performance and execution time are DL and IPP_1. Dose reconstruction on on\u2010board T2\u2010weighted MRIs is feasible with autosegmented structures with minimal dosimetric variation from ground truth, but contours should be visually inspected prior to dose reconstruction in an end\u2010to\u2010end dose accumulation workflow.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.16582","type":"journal-article","created":{"date-parts":[[2023,7,21]],"date-time":"2023-07-21T06:20:14Z","timestamp":1689920414000},"page":"278-291","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Investigation of autosegmentation techniques on T2\u2010weighted MRI for off\u2010line dose reconstruction in MR\u2010linac workflow for head and neck cancers"],"prefix":"10.1002","volume":"51","author":[{"given":"Brigid A.","family":"McDonald","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology The University of Texas MD Anderson Cancer Center Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlos E.","family":"Cardenas","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology The University of Alabama at Birmingham Birmingham Alabama USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicolette","family":"O'Connell","sequence":"additional","affiliation":[{"name":"Elekta AB Stockholm Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sara","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology The University of Texas MD Anderson Cancer Center Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed A.","family":"Naser","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology The University of Texas MD Anderson Cancer Center Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kareem A.","family":"Wahid","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology The University of Texas MD Anderson Cancer Center Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaofeng","family":"Xu","sequence":"additional","affiliation":[{"name":"Elekta AB Stockholm Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dan","family":"Thill","sequence":"additional","affiliation":[{"name":"Elekta AB Stockholm Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raed J.","family":"Zuhour","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology The University of Texas Medical Branch Galveston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shane","family":"Mesko","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology The University of Texas MD Anderson Cancer Center Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Augustyn","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology The University of Texas MD Anderson Cancer Center Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samantha M.","family":"Buszek","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology The University of Texas MD Anderson Cancer Center Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephen","family":"Grant","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology The University of Texas MD Anderson Cancer Center Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bhavana V.","family":"Chapman","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology The University of Texas MD Anderson Cancer Center Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander F.","family":"Bagley","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology The University of Texas MD Anderson Cancer Center Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renjie","family":"He","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology The University of Texas MD Anderson Cancer Center Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdallah S. 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