Classifiers for Ischemic Stroke Lesion Segmentation: A Comparison Study
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MOTIVATION: Ischemic stroke, triggered by an obstruction in the cerebral blood supply, leads to infarction of the affected brain tissue. An accurate and reproducible automatic segmentation is of high interest, since the lesion volume is an important end-point for clinical trials. However, various factors, such as the high variance in lesion shape, location and appearance, render it a difficult task. METHODS: In this article, nine classification methods (e.g. Generalized Linear Models, Random Decision Forests and Convolutional Neural Networks) are evaluated and compared with each other using 37 multiparametric MRI datasets of ischemic stroke patients in the sub-acute phase in terms of their accuracy and reliability for ischemic stroke lesion segmentation. Within this context, a multi-spectral classification approach is compared against mono-spectral classification performance using only FLAIR MRI datasets and two sets of expert segmentations are used for inter-observer agreement evaluation. RESULTS AND CONCLUSION: The results of this study reveal that high-level machine learning methods lead to significantly better segmentation results compared to the rather simple classification methods, pointing towards a difficult non-linear problem. The overall best segmentation results were achieved by a Random Decision Forest and a Convolutional Neural Networks classification approach, even outperforming all previously published results. However, none of the methods tested in this work are capable of achieving results in the range of the human observer agreement and the automatic ischemic stroke lesion segmentation remains a complicated problem that needs to be explored in more detail to improve the segmentation results.
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Maier et al. [55] results showed that cascaded CNN and random decision forest approaches outperform all other methods for ischemic stroke lesion segmentation.
“Their results showed that cascaded CNN and random decision forest approaches outperforms all other methods.”
From Deep learning for brain MRI segmentation: state of the art and future directions (2017) · cites this paper as [55]
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- Authors
- Maier, O., Schröder, C., Forkert, N. D., Martinetz, T., & Handels, H.
- Affiliations
- University of Lübeck (DE)
- University of Calgary (CA)
- Corresponding author
- Maier, Oskar
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2015-12-16
- Volume
- 10
- Issue
- 12
- Pages
- e0145118
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC4687679
- OpenAlex
- W2217077692
- Cited by
- 150
- References
- 41
- Citations by year
- 2026: 4 · 2025: 11 · 2024: 6 · 2023: 9 · 2022: 12 · 2021: 20 · 2020: 28 · 2019: 22 · 2018: 23 · 2017: 9 · 2016: 6
- FWCI
- 11.00
- Type
- journal article
- Language
- en
- Update notices
- correction (2016-02-12)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Health Sciences › Medicine › Epidemiology › Acute Ischemic Stroke Management
- Keywords
- ischemic stroke, lesion segmentation, subacute stroke, FLAIR, convolutional neural networks, random forest, generalized linear models, interobserver agreement, lesion volume
- MeSH terms
- Algorithms, Brain Ischemia, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Trauma Severity Indices, Stroke
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