{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["37(11)"],"submitter":["Storelli L"],"pubmed_abstract":["<h4>Background and purpose</h4>The automatic segmentation of MS lesions could reduce time required for image processing together with inter- and intraoperator variability for research and clinical trials. A multicenter validation of a proposed semiautomatic method for hyperintense MS lesion segmentation on dual-echo MR imaging is presented.<h4>Materials and methods</h4>The classification technique used is based on a region-growing approach starting from manual lesion identification by an expert observer with a final segmentation-refinement step. The method was validated in a cohort of 52 patients with relapsing-remitting MS, with dual-echo images acquired in 6 different European centers.<h4>Results</h4>We found a mathematic expression that made the optimization of the method independent of"],"journal":["AJNR. American journal of neuroradiology"],"pagination":["2043-2049"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7963767"],"repository":["biostudies-literature"],"pubmed_title":["A Semiautomatic Method for Multiple Sclerosis Lesion Segmentation on Dual-Echo MR Imaging: Application in a Multicenter Context."],"pmcid":["PMC7963767"],"pubmed_authors":["Enzinger C","Preziosa P","Pagani E","Filippi M","Thomas DL","Ropele S","Storelli L","Bisecco A","De Stefano N","Battaglini M","Rocca MA","Horsfield MA","Gallo A","Vrenken H","Mancini L"],"additional_accession":[]},"is_claimable":false,"name":"A Semiautomatic Method for Multiple Sclerosis Lesion Segmentation on Dual-Echo MR Imaging: Application in a Multicenter Context.","description":"<h4>Background and purpose</h4>The automatic segmentation of MS lesions could reduce time required for image processing together with inter- and intraoperator variability for research and clinical trials. A multicenter validation of a proposed semiautomatic method for hyperintense MS lesion segmentation on dual-echo MR imaging is presented.<h4>Materials and methods</h4>The classification technique used is based on a region-growing approach starting from manual lesion identification by an expert observer with a final segmentation-refinement step. The method was validated in a cohort of 52 patients with relapsing-remitting MS, with dual-echo images acquired in 6 different European centers.<h4>Results</h4>We found a mathematic expression that made the optimization of the method independent of","dates":{"release":"2016-01-01T00:00:00Z","publication":"2016 Nov","modification":"2025-04-05T15:24:23.648Z","creation":"2025-04-05T15:24:23.648Z"},"accession":"S-EPMC7963767","cross_references":{"pubmed":["27444938"],"doi":["10.3174/ajnr.a4874","10.3174/ajnr.A4874"]}}