Abstract
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 equations. The method extracts a latent atlas prior distribution and the lesion posterior distributions jointly from the image data. It delineates lesion areas individually in each channel, allowing for differences in lesion appearance across modalities, an important feature of many brain tumor imaging sequences. We also propose discriminative model extensions to map the output of the generative model to arbitrary labels with semantic and biological meaning, such as "tumor core" or "fluid-filled structure", but without a one-to-one correspondence to the hypo- or hyper-intense lesion areas identified by the generative model. We test the approach in two image sets: the publicly available BRATS set of glioma patient scans, and multimodal brain images of patients with acute and subacute ischemic stroke. We find the generative model that has been designed for tumor lesions to generalize well to stroke images, and the extended discriminative -discriminative model to be one of the top ranking methods in the BRATS evaluation.
Highlights
G LIOMAS are the most frequent primary brain tumors
We evaluate the relevance of different components and parameters of the probabilistic model, compare it with related generative approaches,and evaluate the performance on the public BRATS glioma dataset, and test the generalization in a transfer to a related application dealing with stroke lesion segmentation
We extend the atlas-based EM segmenter by a latent atlas class that represents the probability of transition from any of the “healthy” tissues to a “lesion” class
Summary
G LIOMAS are the most frequent primary brain tumors They originate from glial cells and grow by infiltrating the surrounding tissue. Glioma is an infiltratively growing tumor with diffuse boundaries and lesion areas are only defined through intensity changes relative to surrounding normal tissues. The mass effect induced by the growing lesion may lead to displacements of the normal brain tissues, as well as a resection cavity that is present after treatment, limits the reliability of prior knowledge available for the healthy parts of the brain. A large variety of imaging modalities can be used for mapping tumor-related tissue changes, providing different types of biological information, such as differences in tissue water (T2-MRI, FLAIR-MRI), enhancement of contrast agents (post-Gadolinium T1-MRI), water diffusion (DTI), blood perfusion (ASL-, DSC-, DCE-MRI), or relative concentrations of selected metabolites (MRSI). A segmentation algorithm must adjust to any of these, without having to collect large training sets, a common limitation for many data-driven learning methods
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