Abstract
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 Maximization-approach 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 level-set formulation is adopted, that eliminates the need for manual initialization of the level-set. The performance of the method for segmenting the ischemic stroke is summarized by an average Dice score of \(0.78\pm 0.08\) and \(0.53 \pm 0.26\) for the SPES and SISS 2015 training data set respectively and \(0.67\pm 0.24\) and \(0.37 \pm 0.33\) for the test data set.
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Haeck, T., Maes, F., Suetens, P. (2016). ISLES Challenge 2015: Automated Model-Based Segmentation of Ischemic Stroke in MR Images. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Handels, H. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2015. Lecture Notes in Computer Science(), vol 9556. Springer, Cham. https://doi.org/10.1007/978-3-319-30858-6_21
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DOI: https://doi.org/10.1007/978-3-319-30858-6_21
Publisher Name: Springer, Cham
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