Senan Doyle

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: Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods, are still trailing human expertise on both detection and delineation criteria, and it is demonstrated that computing a statistically robust consensus of the algorithms performs closer tohuman expertise on one score (segmentation) although still trailing on detection scores.

TL;DR: Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods, are still trailing human expertise on both detection and delineation criteria, and it is demonstrated that computing a statistically robust consensus of the algorithms performs closer tohuman expertise on one score (segmentation) although still trailing on detection scores.

TL;DR: A technique for fusing the output of multiple Magnetic Resonance sequences to robustly and accurately segment brain lesions is proposed, based on a Bayesian multi-sequence Markov model that includes weight parameters to account for the relative importance and control the impact of each sequence.

TL;DR: In this paper, a nonstandard discrete hidden Markov model prior is designed to favor a smooth risk variation, and the model parameters are estimated using an EM algorithm and a mean field approximation for which they develop a new initialization strategy appropriate for spatial Poisson mixtures.