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.
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Appendix
Appendix
Generation of Templates
The common template was done in two phases. First, the initial template was formed:
buildtemplateparallel.sh -d 3 -m 1 \(\mathtt {\times }\) 0 \(\mathtt {\times }\) 0 -n 0 -r 1 -t GR -s CC -o [initial template image] -c 0 -j 1 [T1 images]
After that, the final template was built using the initial template:
buildtemplateparallel.sh -d 3 -m 30 \(\mathtt {\times }\) 90 \(\mathtt {\times }\) 20 -n 0 -r 0 -t GR -s CC -o [template image] -z [initial template image] -c 0 [T1 images]
Warping of T1 images to common template was done using antsRegistration tool and the following parameters:
--metric MI[template image, T1 image,1,32] --transform affine[0.25] --convergence 10000 \(\mathtt {\times }\) 10000 \(\mathtt {\times }\) 10000 \(\mathtt {\times }\) 10000 \(\mathtt {\times }\) 10000 --shrink factors 5 \(\mathtt {\times }\) 4 \(\mathtt {\times }\) 3 \(\mathtt {\times }\) 2 \(\mathtt {\times }\) 1 --smoothing-sigmas 4 \(\mathtt {\times }\) 3 \(\mathtt {\times }\) 2 \(\mathtt {\times }\) 1 \(\mathtt {\times }\) 0 --metric CC[template image, T1 image,1,5] --transform SyN[0.25,3.0,0.0] --convergence 50 \(\mathtt {\times }\) 35 \(\mathtt {\times }\) 15 --shrink factors 3 \(\mathtt {\times }\) 2 \(\mathtt {\times }\) 1 --smoothing-sigmas 2 \(\mathtt {\times }\) 1 \(\mathtt {\times }\) 0 --use-histogram-matching 1 --x [lesion image]
Parameters for Random Forest Classifier
Scikit-learn’s function sklearn.ensemble.RandomForestClassifier was used with the following parameters:
\({\texttt {n\_estimators=300, criterion='gini', max\_depth=None, min\_samples\_}}\) \({\texttt {split=2, min\_samples\_leaf=1, min\_weight\_fraction\_leaf=0.0, max\_}}\) \({\texttt {features=4, max\_leaf\_nodes=None, bootstrap=True, oob\_score=False,}}\) \({\texttt {n\_jobs=1, random\_state=None, verbose=0, warm\_start=False,}}\) \({\texttt {class\_weight=None}}\)
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Halme, HL., Korvenoja, A., Salli, E. (2016). ISLES (SISS) Challenge 2015: Segmentation of Stroke Lesions Using Spatial Normalization, Random Forest Classification and Contextual Clustering. 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_18
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DOI: https://doi.org/10.1007/978-3-319-30858-6_18
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