Computer Vision News Computer Vision News 4 MIDL Best Oral Paper Award Winner SINR: Spline-enhanced implicit neural representation for multi-modal registration Deformable image registration aims to align two images by determining a transformation for every pixel rather than a global transformation, such as rotation or translation. Recovering these transformations at a granular level is particularly useful in medical scenarios. For instance, in cardiac imaging, different time points of the heart beating could be registered to detect abnormalities. In this paper, Vasiliki is working with brain images. “Registration is challenging when you want to recover the transformation at every point in space because there is more than one possible solution,” she explains. “We don’t have any ground truth for this, so we don’t know what we’re looking for. As a result, we have to do this fully unsupervised. We have to do this in a way that we have surrogate Vasiliki Sideri-Lampretsa is a PhD student in Daniel Rueckert’s AI in Medicine Lab at the Technical University of Munich. Fresh from winning the Best Oral Award at MIDL 2024, Vasiliki speaks to us about her novel approach to deformable image registration and its significant promise for clinical practice.
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