ArticleNeuroinformatics2015
Optimal Symmetric Multimodal Templates and Concatenated Random Forests for Supervised Brain Tumor Segmentation (Simplified) with ANTsR.
Article in Neuroinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 2 of them syntheses that pooled it.
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Who cites it
48 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Performance of machine learning algorithms for glioma segmentation of brain MRI: a systematic literature review and meta-analysis.European radiology · 2021Pooled it
- Automated Segmentation of Tissues Using CT and MRI: A Systematic Review.Academic radiology · 2019Pooled it
- The ANTsX ecosystem for mapping the mouse brain.Nature communications · 2025Article
- Modular strategies for spatial mapping of diverse cell type data of the mouse brain.Research square · 2025Article
- An intelligent LinkNet-34 model with EfficientNetB7 encoder for semantic segmentation of brain tumor.Scientific reports · 2024Article
- Automatic segmentation of brain glioma based on XY-Net.Medical & biological engineering & computing · 2024Article
- Article
- Internally consistent and fully unbiased multimodal MRI brain template construction from UK Biobank: Oxford-MM.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Beyond Imaging and Genetic Signature in Glioblastoma: Radiogenomic Holistic Approach in Neuro-Oncology.Biomedicines · 2022Review
- Detection on Cell Cancer Using the Deep Transfer Learning and Histogram Based Image Focus Quality Assessment.Sensors (Basel, Switzerland) · 2022Article
- Brain MR Atlas Construction Using Symmetric Deep Neural Inpainting.IEEE journal of biomedical and health informatics · 2022Article
- Prediction of glioma-subtypes: comparison of performance on a DL classifier using bounding box areas versus annotated tumors.BMC biomedical engineering · 2022Article
- Testing the accuracy of 3D automatic landmarking via genome-wide association studies.G3 (Bethesda, Md.) · 2022Article
- 3D AGSE-VNet: an automatic brain tumor MRI data segmentation framework.BMC medical imaging · 2022Article
- Automatic Segmentation of MRI of Brain Tumor Using Deep Convolutional Network.BioMed research international · 2022Article
- Residual Block Based Nested U-Type Architecture for Multi-Modal Brain Tumor Image Segmentation.Frontiers in neuroscience · 2022Article
- Optimal Superpixel Kernel-Based Kernel Low-Rank and Sparsity Representation for Brain Tumour Segmentation.Computational intelligence and neuroscience · 2022Article
- Simultaneous brain structure segmentation in magnetic resonance images using deep convolutional neural networks.Radiological physics and technology · 2021Article
- Analysis of Brain MRI Images Using Improved CornerNet Approach.Diagnostics (Basel, Switzerland) · 2021Article
- Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.