Background: Activity of disease in patients with multiple sclerosis (MS) is monitored by detecting and delineating\r\nhyper-intense lesions on MRI scans. The Minimum Area Contour Change (MACC) algorithm has been created with\r\ntwo main goals: a) to improve inter-operator agreement on outlining regions of interest (ROIs) and b) to\r\nautomatically propagate longitudinal ROIs from the baseline scan to a follow-up scan.\r\nMethods: The MACC algorithm first identifies an outer bound for the solution path, forms a high number of\r\niso-contour curves based on equally spaced contour values, and then selects the best contour value to outline the\r\nlesion. The MACC software was tested on a set of 17 FLAIR MRI images evaluated by a pair of human experts and a\r\nlongitudinal dataset of 12 pairs of T2-weighted Fluid Attenuated Inversion Recovery (FLAIR) images that had lesion\r\nanalysis ROIs drawn by a single expert operator.\r\nResults: In the tests where two human experts evaluated the same MRI images, the MACC program demonstrated\r\nthat it could markedly reduce inter-operator outline error. In the longitudinal part of the study, the MACC program\r\ncreated ROIs on follow-up scans that were in close agreement to the original expert�s ROIs. Finally, in a post-hoc\r\nanalysis of 424 follow-up scans 91% of propagated MACC were accepted by an expert and only 9% of the final\r\naccepted ROIS had to be created or edited by the expert.\r\nConclusion: When used with an expert operator''s verification of automatically created ROIs, MACC can be used to\r\nimprove inter- operator agreement and decrease analysis time, which should improve data collected and analyzed\r\nin multicenter clinical trials.
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