{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T02:06:29Z","timestamp":1785377189031,"version":"3.55.0"},"reference-count":64,"publisher":"IOP Publishing","issue":"11","license":[{"start":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T00:00:00Z","timestamp":1747958400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T00:00:00Z","timestamp":1747958400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100007923","name":"University of Wisconsin Carbone Cancer Center","doi-asserted-by":"crossref","award":["P30 CA014520"],"award-info":[{"award-number":["P30 CA014520"]}],"id":[{"id":"10.13039\/100007923","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2025,6,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>\n                  <jats:italic>Objective.<\/jats:italic> Deep learning models are increasingly being implemented for automated medical image analysis to inform patient care. Most models, however, lack uncertainty information, without which the reliability of model outputs cannot be ensured. Several uncertainty quantification (UQ) methods exist to capture model uncertainty. Yet, it is not clear which method is optimal for a given task. The purpose of this work was to investigate several commonly used UQ methods for the critical yet understudied task of metastatic lesion segmentation on whole body PET\/CT. <jats:italic>Approach.<\/jats:italic> 59 whole body <jats:sup>68<\/jats:sup>Ga-DOTATATE PET\/CT images of patients undergoing theranostic treatment of metastatic neuroendocrine tumors were used in this work. A 3D U-Net was trained for lesion segmentation following five-fold cross validation. Uncertainty measures derived from four UQ methods\u2014probability entropy, Monte Carlo dropout, deep ensembles, and test time augmentation\u2014were investigated. Each uncertainty measure was assessed across four quantitative evaluations: (1) its ability to detect artificially degraded image data at low, medium, and high degradation magnitudes; (2) to detect false-positive (FP) predicted regions; (3) to recover false-negative (FN) predicted regions; and (4) to establish correlations with model biomarker extraction and segmentation performance metrics. <jats:italic>Main<\/jats:italic>\n                  <jats:italic>results.<\/jats:italic> Test time augmentation and probability entropy respectively achieved the highest and lowest degraded image detection at low (AUC = 0.54 vs. 0.68), medium (AUC = 0.70 vs. 0.82), and high (AUC = 0.83 vs. 0.90) degradation magnitudes. For detecting FPs, all UQ methods achieve strong performance, with AUC values ranging narrowly between 0.77 and 0.81. FN region recovery performance was strongest for test time augmentation and weakest for probability entropy. Performance for the correlation analysis was mixed, where the strongest performance was achieved by test time augmentation for SUV<jats:sub>total<\/jats:sub> capture (<jats:inline-formula>\n                     <jats:tex-math\/>\n                     <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" overflow=\"scroll\">\n                        <mml:mrow>\n                           <mml:mi>\u03c1<\/mml:mi>\n                        <\/mml:mrow>\n                     <\/mml:math>\n                  <\/jats:inline-formula> = 0.57) and segmentation Dice coefficient (<jats:inline-formula>\n                     <jats:tex-math\/>\n                     <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" overflow=\"scroll\">\n                        <mml:mrow>\n                           <mml:mi>\u03c1<\/mml:mi>\n                        <\/mml:mrow>\n                     <\/mml:math>\n                  <\/jats:inline-formula> = 0.72), by Monte Carlo dropout for SUV<jats:sub>mean<\/jats:sub> capture (<jats:inline-formula>\n                     <jats:tex-math\/>\n                     <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" overflow=\"scroll\">\n                        <mml:mrow>\n                           <mml:mi>\u03c1<\/mml:mi>\n                        <\/mml:mrow>\n                     <\/mml:math>\n                  <\/jats:inline-formula> = 0.35), and by probability entropy for segmentation cross entropy (<jats:inline-formula>\n                     <jats:tex-math\/>\n                     <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" overflow=\"scroll\">\n                        <mml:mrow>\n                           <mml:mi>\u03c1<\/mml:mi>\n                        <\/mml:mrow>\n                     <\/mml:math>\n                  <\/jats:inline-formula> = 0.96). <jats:italic>Significance.<\/jats:italic> Overall, test time augmentation demonstrated superior UQ performance and is recommended for use in metastatic lesion segmentation task. It also offers the advantage of being post hoc and computationally efficient. In contrast, probability entropy performed the worst, highlighting the need for advanced UQ approaches for this task.<\/jats:p>","DOI":"10.1088\/1361-6560\/add9df","type":"journal-article","created":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T22:53:02Z","timestamp":1747435982000},"page":"115009","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Uncertainty quantification for deep learning-based metastatic lesion segmentation on whole body PET\/CT"],"prefix":"10.1088","volume":"70","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-3326-1854","authenticated-orcid":true,"given":"Brayden","family":"Schott","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6965-0448","authenticated-orcid":false,"given":"Victor","family":"Santoro-Fernandes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3409-7771","authenticated-orcid":false,"given":"\u017dan","family":"Klane\u010dek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9744-7105","authenticated-orcid":false,"given":"Scott","family":"Perlman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2192-2931","authenticated-orcid":true,"given":"Robert","family":"Jeraj","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2025,5,23]]},"reference":[{"key":"pmbadd9dfbib1","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1007\/978-3-031-44336-7_6","article-title":"Benchmarking scalable epistemic uncertainty quantification in organ segmentation","volume":"14291","author":"Adams","year":"2023","journal-title":"Uncertain. 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