{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T18:09:58Z","timestamp":1790273398359,"version":"4.1.0"},"reference-count":0,"publisher":"SPIE-Intl Soc Optical Eng","issue":"01","funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["3U54CK120058"],"award-info":[{"award-number":["3U54CK120058"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006108","name":"National Institutes of Health (NIH)\/National Center for Advancing Translational Sciences","doi-asserted-by":"publisher","award":["ULTR000445"],"award-info":[{"award-number":["ULTR000445"]}],"id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1452485"],"award-info":[{"award-number":["1452485"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NSF CAREER","award":["1452485"],"award-info":[{"award-number":["1452485"]}]},{"name":"National Science Foundation CAREER","award":["1452485"],"award-info":[{"award-number":["1452485"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Med. Imag."],"published-print":{"date-parts":[[2021,2,19]]},"abstract":"<jats:p>Purpose: Deep learning is a promising technique for spleen segmentation. Our study aims to validate the reproducibility of deep learning-based spleen volume estimation by performing spleen segmentation on clinically acquired computed tomography (CT) scans from patients with myeloproliferative neoplasms.\nApproach: As approved by the institutional review board, we obtained 138 de-identified abdominal CT scans. A sum of voxel volume on an expert annotator\u2019s segmentations establishes the ground truth (estimation 1). We used our deep convolutional neural network (estimation 2) alongside traditional linear estimations (estimation 3 and 4) to estimate spleen volumes independently. Dice coefficient, Hausdorff distance, R2 coefficient, Pearson R coefficient, the absolute difference in volume, and the relative difference in volume were calculated for 2 to 4 against the ground truth to compare and assess methods\u2019 performances. We re-labeled on scan\u2013rescan on a subset of 40 studies to evaluate method reproducibility.\nResults: Calculated against the ground truth, the R2 coefficients for our method (estimation 2) and linear method (estimation 3 and 4) are 0.998, 0.954, and 0.973, respectively. The Pearson R coefficients for the estimations against the ground truth are 0.999, 0.963, and 0.978, respectively (paired t-tests produced p\u2009\u2009&lt;\u2009\u20090.05 between 2 and 3, and 2 and 4).\nConclusion: The deep convolutional neural network algorithm shows excellent potential in rendering more precise spleen volume estimations. Our computer-aided segmentation exhibits reasonable improvements in splenic volume estimation accuracy.<\/jats:p>","DOI":"10.1117\/1.jmi.8.1.014004","type":"journal-article","created":{"date-parts":[[2021,2,19]],"date-time":"2021-02-19T03:10:00Z","timestamp":1613704200000},"page":"1-11","source":"Crossref","is-referenced-by-count":8,"title":["Validation and estimation of spleen volume via computer-assisted segmentation on clinically acquired CT scans"],"prefix":"10.1117","volume":"8","author":[{"given":"Yiyuan","family":"Yang","sequence":"first","affiliation":[{"name":"Vanderbilt Univ."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yucheng","family":"Tang","sequence":"additional","affiliation":[{"name":"Vanderbilt Univ."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8729-1941","authenticated-orcid":false,"given":"Riqiang","family":"Gao","sequence":"additional","affiliation":[{"name":"Vanderbilt Univ."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6376-4292","authenticated-orcid":false,"given":"Shunxing","family":"Bao","sequence":"additional","affiliation":[{"name":"Vanderbilt Univ."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2096-8065","authenticated-orcid":false,"given":"Yuankai","family":"Huo","sequence":"additional","affiliation":[{"name":"Vanderbilt Univ."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5003-539X","authenticated-orcid":false,"given":"Matthew T.","family":"McKenna","sequence":"additional","affiliation":[{"name":"Vanderbilt-Ingram Cancer Ctr."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael R.","family":"Savona","sequence":"additional","affiliation":[{"name":"Vanderbilt-Ingram Cancer Ctr."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1200-0281","authenticated-orcid":false,"given":"Richard G.","family":"Abramson","sequence":"additional","affiliation":[{"name":"HCA Healthcare"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5733-2127","authenticated-orcid":false,"given":"Bennett A.","family":"Landman","sequence":"additional","affiliation":[{"name":"Vanderbilt Univ."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"189","container-title":["Journal of Medical Imaging"],"original-title":[],"link":[{"URL":"https:\/\/www.spiedigitallibrary.org\/journals\/journal-of-medical-imaging\/volume-8\/issue-01\/014004\/Validation-and-estimation-of-spleen-volume-via-computer-assisted-segmentation\/10.1117\/1.JMI.8.1.014004.full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T17:40:26Z","timestamp":1790271626000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.spiedigitallibrary.org\/journals\/journal-of-medical-imaging\/volume-8\/issue-01\/014004\/Validation-and-estimation-of-spleen-volume-via-computer-assisted-segmentation\/10.1117\/1.JMI.8.1.014004.full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,19]]},"references-count":0,"journal-issue":{"issue":"01"},"URL":"https:\/\/doi.org\/10.1117\/1.jmi.8.1.014004","relation":{},"ISSN":["2329-4302","2329-4310"],"issn-type":[{"value":"2329-4302","type":"print"},{"value":"2329-4310","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,19]]}}}