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

PROJECT TITLE :

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

ABSTRACT:

We have a tendency to introduce a generative probabilistic model for segmentation of brain lesions in multi-dimensional images that generalizes the EM segmenter, a typical approach for modelling brain pictures using Gaussian mixtures and a probabilistic tissue atlas that employs expectation-maximization (EM), to estimate the label map for a replacement 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-kind EM update equations. The strategy extracts a latent atlas previous distribution and also the lesion posterior distributions jointly from the image information. It delineates lesion areas individually in every channel, allowing for variations in lesion appearance across modalities, an important feature of many brain tumor imaging sequences. We additionally propose discriminative model extensions to map the output of the generative model to arbitrary labels with semantic and biological that means, like “tumor core” or “fluid-filled structure”, however while not a one-to-one correspondence to the hypo- or hyper-intense lesion areas identified by the generative model. We test the approach in 2 image sets: the publicly on the market BRATS set of glioma patient scans, and multimodal brain pictures of patients with acute and subacute ischemic stroke. We notice the generative model that has been designed for tumor lesions to generalize well to stroke pictures, and also the extended discriminative -discriminative model to be one in every of the prime ranking strategies in the BRATS evaluation.

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