Although Digital Subtraction Angiography (DSA) is the most important imaging for visualizing cerebrovascular anatomy, its interpretation by clinicians remains difficult. This is particularly true when treating arteriovenous malformations (AVMs), where entangled vasculature connecting arteries and veins needs to be carefully identified. The presented method aims to enhance DSA image series by highlighting critical information via automatic classification of vessels using a combination of two learning models: An unsupervised machine learning method based on Independent Component Analysis that decomposes the phases of flow and a convolutional neural network that automatically delineates the vessels in image space. The proposed method was tested on clinical DSA images series and demonstrated efficient differentiation between arteries and veins that provides a viable solution to enhance visualizations for clinical use.
Non-linear registration models optimize two conflicting objectives, a content-matching term and a deformation smoothness measure. As the desired smoothness regime is problem-specific, there is a need to better compare generic registration algorithms across different smoothness regimes. We propose to compare registration algorithms by estimating their content-matching vs deformation smoothness Pareto front. Specifically, we assess the deformation smoothness level reached by each algorithm at different content-matching levels. We introduce a new objective function to sample the Pareto front along a specific iso-content-matching line. We demonstrate the applicability of our method on chest-CT inter-patient registration by comparing 5 learning-based registration algorithms.
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