Quasiconformal Registration Network (QCRN) for large deformation image registration

This work presents a novel deep neural network with the incorporation of quasiconformal (QC) theories for large deformation image registration. Image registration is a key process in many practical tasks in computer visions, computer graphics and medical imaging. It is a challenging problem especially when the deformation between images is large. The problem is often formulated as an optimization problem, which is computationally intensive and time consuming. To accelerate the process, training deep neural networks for image registration has recently attracted much attention and shown satisfactory results while requiring signficiantly less computational time. Nevertheless, under these deep learning frameworks, the geometric distortion under the registration map cannot be controlled. To address this issue, we propose a novel deep neural network based on quasiconformal (QC) theories. Instead of learning the vector fields for spatial transformation, our network learns a geometric quantity, called the Beltrami coefficient, to obtain the registration map. The Beltrami coefficient effectively measures and controls the local geometric distortions under the registration map. Thus, our network can obtain diffeomorphic image registration, even with very large deformation. Extensive experiments on real images demonstrate the effectiveness of our method.

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Animation: From Moving Image to Target Image

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Animation: From Moving Image to Target Image

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Animation: From Moving Image to Target Image