Gradient-Based Expanding Spherical Appearance Models for Femoral Model Initialization in MRI
While deep learning strategies for semantic segmentation increasingly take center stage, traditional approaches seem to take a backseat. However, in the domain of medical image processing, labeled training data is rare and expensive to acquire. Thus, traditional methods may still be preferable to deep learning approaches. Many of these conventional approaches often require initial localization of the structure of interest (SOI) to provide satisfactory results. In this work we present a fully automatic model initialization approach in MRI, that is applicable for anatomical structures that contain a near-spherical component. We propose a model, that encapsulates the difference between intensity distribution within the SOI’s spherical component and its proximity. We present our approach on the example of femoral model initialization and compare our initialization results to a diffeomorphic demons registration approach.
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