INSPIRE: Intensity and Spatial Information-Based Deformable Image Registration
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We present INSPIRE, a top-performing general-purpose method for deformable image registration. INSPIRE extends our existing symmetric registration framework based on distances combining intensity and spatial information to an elastic B-splines based transformation model. We also present several theoretical and algorithmic improvements which provide high computational efficiency and thereby applicability of the framework in a wide range of real scenarios. We show that the proposed method delivers both highly accurate as well as stable and robust registration results. We evaluate the method on a synthetic dataset created from retinal images, consisting of thin networks of vessels, where INSPIRE exhibits excellent performance, substantially outperforming the reference methods. We also evaluate the method on four benchmark datasets of 3D images of brains, for a total of 2088 pairwise registrations; a comparison with 15 other state-of-the-art methods reveals that INSPIRE provides the best overall performance. Code is available at github.com/MIDA-group/inspire.
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