Optimal Symmetric Multimodal Templates and Concatenated Random Forests for Supervised Brain Tumor Segmentation (Simplified) with ANTsR
| Autor: | Benjamin M. Kandel, Max Wintermark, K. L. Shrinidhi, James C. Gee, Nicholas J. Tustison, Brian B. Avants, Murray Grossman, Christopher R. Durst |
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| Rok vydání: | 2014 |
| Předmět: |
Computer science
Scale-space segmentation Machine learning computer.software_genre Sensitivity and Specificity Pattern Recognition Automated Discriminative model Image Interpretation Computer-Assisted Humans Segmentation Markov random field Brain Neoplasms business.industry General Neuroscience Supervised learning Reproducibility of Results Pattern recognition Models Theoretical Random forest Feature (computer vision) Pattern recognition (psychology) Artificial intelligence business computer Algorithms Software Information Systems |
| Zdroj: | Neuroinformatics. 13:209-225 |
| ISSN: | 1559-0089 1539-2791 |
| Popis: | Segmenting and quantifying gliomas from MRI is an important task for diagnosis, planning intervention, and for tracking tumor changes over time. However, this task is complicated by the lack of prior knowledge concerning tumor location, spatial extent, shape, possible displacement of normal tissue, and intensity signature. To accommodate such complications, we introduce a framework for supervised segmentation based on multiple modality intensity, geometry, and asymmetry feature sets. These features drive a supervised whole-brain and tumor segmentation approach based on random forest-derived probabilities. The asymmetry-related features (based on optimal symmetric multimodal templates) demonstrate excellent discriminative properties within this framework. We also gain performance by generating probability maps from random forest models and using these maps for a refining Markov random field regularized probabilistic segmentation. This strategy allows us to interface the supervised learning capabilities of the random forest model with regularized probabilistic segmentation using the recently developed ANTsR package--a comprehensive statistical and visualization interface between the popular Advanced Normalization Tools (ANTs) and the R statistical project. The reported algorithmic framework was the top-performing entry in the MICCAI 2013 Multimodal Brain Tumor Segmentation challenge. The challenge data were widely varying consisting of both high-grade and low-grade glioma tumor four-modality MRI from five different institutions. Average Dice overlap measures for the final algorithmic assessment were 0.87, 0.78, and 0.74 for "complete", "core", and "enhanced" tumor components, respectively. |
| Databáze: | OpenAIRE |
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