Structure-guided registration in learning based image analysis

Matthew Chung Hai Lee, Ben Glocker
Image registration is a key component in many medical image analysis pipelines and is useful in general computer vision applications. The goal of image registration is to find a transformation between the coordinate spaces of two images, such that the transformation aligns some Structure-of-Interest which exist in both images. Object tracking, image segmentation, multi-modal data fusion, longitudinal studies, label propagation, image labelling, population studies, image stitching and voxel
more » ... morphometry either rely on or at least benefit from image registration. In this thesis, three aspects of image registration are discussed. Firstly, we utilise image registration to perform image segmentation via template deformation, the registration of some prior shape model with an image. We utilise neural networks to perform this template registration, the networks implicitly embed Structure-of-Interest information during training, to utilise this during inference when Structure-of-Interest information is not readily available. This differs from the conventional template deformation paradigm, where one must construct some image to segmentation likelihood function for the registration algorithm, a proxy function for the true segmentation accuracy. Utilising neural networks circumvents having to do this, we are able to train a network directly using a segmentation loss without hand crafting such a loss function. Our method gives us the prior enforcing benefits of template deformations without the difficulty of deriving some approximation to the segmentation loss. Secondly, we develop a framework for combining iterative image registration with neural network based representation learning. Recent network based image registration has generally focused on improving the speed of registration, as neural networks are able to predict deformation fields in one shot, rather than iteratively converging during test time. We argue however that this comes at the cost of the accuracy of the registration. We propose a method that extrac [...]
doi:10.25560/79296 fatcat:rldmsq3fefabpkurupwmi3yu3u