Liang Zhao

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 MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.

TL;DR: A robust segmentation method using model-aware affinity demonstrates comparable performance with other state-of-the art algorithms for brain tumor MRI scans.

TL;DR: The key module of the proposed framework is a 3-D FCN trained in an end-to-end manner at the spine level to capture the long-range contextual information in CT volumes that enables the integration of local image details and global image patterns.