Decision Forests for Tissue-Specific Segmentation of High-Grade Gliomas in Multi-channel MR
Open accessAbstract
We present a method for automatic segmentation of high-grade gliomas and their subregions from multi-channel MR images. Besides segmenting the gross tumor, we also differentiate between active cells, necrotic core, and edema. Our discriminative approach is based on decision forests using context-aware spatial features, and integrates a generative model of tissue appearance, by using the probabilities obtained by tissue-specific Gaussian mixture models as additional input for the forest. Our method classifies the individual tissue types simultaneously, which has the potential to simplify the classification task. The approach is computationally efficient and of low model complexity. The validation is performed on a labeled database of 40 multi-channel MR images, including DTI. We assess the effects of using DTI, and varying the amount of training data. Our segmentation results are highly accurate, and compare favorably to the state of the art.
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Research works printed but not linked: 8
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DALSA: domain adaptation for supervised learning from sparsely annotated MR images
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- Authors
- Zikic, D., Glocker, B., Konukoglu, E., Criminisi, A., Demiralp, C., Shotton, J., Thomas, O. M., Das, T., Jena, R., & Price, S. J.
- Affiliations
- Microsoft Research (United Kingdom) (GB)
- University of Cambridge (GB)
- Cambridge University Hospitals NHS Foundation Trust (GB)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2012
- Volume
- 15
- Issue
- Pt 3
- Pages
- 369-376
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1524094261
- Cited by
- 276
- References
- 0
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 10 · 2023: 17 · 2022: 22 · 2021: 27 · 2020: 22 · 2019: 33 · 2018: 23 · 2017: 21 · 2016: 34 · 2015: 14 · 2014: 26 · 2013: 15
- FWCI
- 12.50
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Lecture notes in computer science (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute for Health and Care Research (NIHR/CS/009/011)
- NIHR Cambridge Biomedical Research Centre
- 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 › Genetics › Glioma Diagnosis and Treatment
- Keywords
- high-grade glioma, brain tumor segmentation, decision forest, multi-channel MRI, diffusion tensor imaging, tissue classification, necrotic core, edema, Gaussian mixture model
- MeSH terms
- Algorithms, Brain, Brain Neoplasms, Glioma, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Pattern Recognition, Automated, Sensitivity and Specificity, Reproducibility of Results, Decision Support Systems, Clinical
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