A Generative Probabilistic Model and Discriminative Extensions for Brain Lesion Segmentation— With Application to Tumor and Stroke

@article{Menze2016AGP,
  title={A Generative Probabilistic Model and Discriminative Extensions for Brain Lesion Segmentation— With Application to Tumor and Stroke},
  author={Bjoern H. Menze and Koenraad Van Leemput and Danial Lashkari and Tammy Riklin-Raviv and Ezequiel Geremia and Esther Alberts and Philipp Gruber and Susanne Wegener and Marc-Andr{\'e} Weber and G{\'a}bor Sz{\'e}kely and Nicholas Ayache and Polina Golland},
  journal={IEEE Transactions on Medical Imaging},
  year={2016},
  volume={35},
  pages={933-946}
}
We introduce a generative probabilistic model for segmentation of brain lesions in multi-dimensional images that generalizes the EM segmenter, a common approach for modelling brain images using Gaussian mixtures and a probabilistic tissue atlas that employs expectation-maximization (EM), to estimate the label map for a new image. Our model augments the probabilistic atlas of the healthy tissues with a latent atlas of the lesion. We derive an estimation algorithm with closed-form EM update… CONTINUE READING