ISLES Challenge 2015: Automated Model-Based Segmentation of Ischemic Stroke in MR Images [chapter]

Tom Haeck, Frederik Maes, Paul Suetens
<span title="">2016</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
We present a novel fully-automated generative ischemic stroke lesion segmentation method that can be applied to individual patient images without need for a training data set. An Expectation Maximizationapproach is used for estimating intensity models for both normal and pathological tissue. The segmentation is represented by a level-set that is iteratively updated to label voxels as either normal or pathological, based on which intensity model explains the voxels' intensity the best. A convex
more &raquo; ... evel-set formulation is adopted, that eliminates the need for manual initialization of the the level-set. The performance of the method for segmenting the ischemic stroke is summarized by an average Dice score of 0.78 and 0.51 for the SPES and SISS 2015 training set respectively.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.1007/978-3-319-30858-6_21</a> <a target="_blank" rel="external noopener" href="">fatcat:frfomcsq5ndqdgztf4ks4hgwgy</a> </span>
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