In medical imaging, finding landmarks that provide biologically meaningful correspondences is often a challenging and time-consuming manual task. In this paper we propose a generic and simple algorithm for landmarking non-cortical brain structures automatically. We use a probabilistic model of the image intensities based on the deformation of a tissue probability map, learned from a training set of hand-landmarked images. In this setting, estimating the location of the landmarks in a new image is equivalent to finding, by likelihood maximization, the "best" deformation from the tissue probability map to the image. The resulting algorithm is able to handle arbitrary types and numbers of landmarks. We demonstrate our algorithm on the detection of 3 landmarks of the hippocampus in brain MR images.
Landmarking MR images is crucial in registering brain structures from different images. It consists in locating the voxel in the image that corresponds to a well-defined point in the anatomy, called the landmark. Example of landmarks are the apex of the head (HoH) of the Hippocampus, the tail and the tip of the splenium of the corpus collosum (SCC). Hand landmarking is tedious and time-consuming. It requires an adequate training. Experimental studies show that the results are dependent on the landmarker and drifting with time. We propose a generic algorithm performing semi-automated detection of landmarks. The first part consists in learning from a training set of landmarked images the parameters of a probabilistic model, using the EM algorithm. The second part inputs the estimated parameters and a new image, and outputs a voxel as a predicted location for the landmark. The algorithm is demonstrated on the HoH and the SCC. In contrast with competing approaches, the algorithm is generic: it can be used to detect any landmark as soon as a collection of hand-landmarked images is provided for training.
In order to perform medical image registration, landmarks are used to settle correspondences between images. A landmark is a voxel in the image that corresponds to a well-defined point in the anatomy. Manual landmarking is a difficult, tedious and time-consuming procedure that would gain to be automated. We propose a bayesian approach for automatic landmarking. Using training data, we learn the geometry through a probabilistic template. Landmarking consists then in estimating an affine transformation mapping the image onto the template. We use gradient ascent in the likelihood function to perform this task. Experiments validate the methodology for landmarking the temporal lobe in MR brain images.
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