Abstract
Automated methods for segmentation of ischemic stroke lesions could significantly reduce the workload of radiologists and speed up the beginning of patient treatment. In this paper, we present a method for subacute ischemic stroke lesion segmentation from multispectral magnetic resonance images (MRI). The method involves classification of voxels with a Random Forest algorithm and subsequent classification refinement with contextual clustering. In addition, we utilize the training data to build statistical group-specific templates and use them for calculation of individual voxel-wise differences from the global mean. Our method achieved a Dice coefficient of 0.61 for the leave-one-out cross-validated training data and 0.47 for the testing data of the ISLES challenge 2015.
Original language | English |
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Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Publisher | Springer |
Pages | 211-221 |
Number of pages | 11 |
Volume | 9556 |
ISBN (Print) | 9783319308579 |
DOIs | |
Publication status | Published - 2016 |
MoE publication type | A4 Conference publication |
Event | International Workshop on Brainlesion - Munich, Germany Duration: 5 Oct 2015 → 5 Oct 2015 Conference number: 1 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 9556 |
ISSN (Print) | 03029743 |
ISSN (Electronic) | 16113349 |
Workshop
Workshop | International Workshop on Brainlesion |
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Abbreviated title | Brainles |
Country/Territory | Germany |
City | Munich |
Period | 05/10/2015 → 05/10/2015 |