Abstract
We propose a fully automatic method for segmenting the ischemic penumbra, using image texture and spatial features and a modified Random Forest algorithm, which we call Segmentation Forests, which has been designed to adapt the original Random Forests algorithm of Breiman to the segmentation of medical images. The method was trained and tested on the SPES dataset, part of the ISLES MICCAI Grand Challenge. The method is fast, taking approximately six minutes to segment a new case, and yields convincing results. On the testing portion of the SPES dataset, the method achieved an average Dice coefficient of 0.82, with a standard deviation of 0.08.
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Acknowledgments
The authors would like to thank the organizers of the ISLES challenge, and the Brainles Workshop, both part of MICCAI 2015. This work was supported by the Schweizerische Herzstiftung.
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McKinley, R., Häni, L., Wiest, R., Reyes, M. (2016). Segmenting the Ischemic Penumbra: A Decision Forest Approach with Automatic Threshold Finding. 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_24
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DOI: https://doi.org/10.1007/978-3-319-30858-6_24
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-30857-9
Online ISBN: 978-3-319-30858-6
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