Stefan Bauer

TL;DR: The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) as mentioned in this paper was organized in conjunction with the MICCAI 2012 and 2013 conferences, and twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low and high grade glioma patients.

TL;DR: This study assesses the state-of-the-art machine learning methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018, and investigates the challenge of identifying the best ML algorithms for each of these tasks.

TL;DR: The state of the art in segmentation, registration and modeling related to tumor-bearing brain images with a focus on gliomas is reviewed, giving special attention to recent developments in radiological tumor assessment guidelines.

TL;DR: A fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields, which is fast and tailored to standard clinical acquisition protocols.

TL;DR: The automated volume measurements were comparable to manual tumor delineation for CETV tumor volumes, and outperformed inter-rater variability for overlap and sensitivity and Spearman's rank correlation coefficients of TV+, TV and CETV showed highly significant correlations between automatic and manual segmentations.