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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...
Image registration is used extensively in medical imaging. Visual assessment of its quality is time consuming and not necessarily accurate. Automatic estimation of registration accuracy is desired for many clinical applications. Current methods rely on learning a relationship between image features and registration error. In this paper we propose an unsupervised method for the detection of local registration...
Simulating synthetic image data is crucial for the design and validation of bio-imaging pipelines. Most of the existing frameworks assume objects in pixel space, whereas most of the spatio-temporal models of biological processes are formulated in object space. We show that the key to a physically-principled synthetic image data simulation engine is to model carefully the mapping between objects and...
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