| Summary: | 碩士 === 國立臺灣大學 === 資訊工程學研究所 === 107 === Deformable image registration, which establishes a non-linear correspondence between a pair of images, is widly use a fundamental step in many medical image analysis procedures. Conventional registration methods, which
iteratively solve an optimization problem, can be very slow and hinder the
practical applications. To this end, formulating it as a learning based problem
using Convolutional Neural Networks (CNNs) can largely reduce the registration time [3]. Most of existing learning-based methods directly predict a
dense deformation field from an input pair of fixed and moving images. However, the resulting transformation could be physically implausible, and selffolding cannot be avoided. In this work, we model the deformation with Bspline transformation which intrinsically produces smooth deformation, and
the proposed local invertibility constraint largely alleviates the self-folding
issue in the resulting deformation field. We also present that our multi-scale
learning framework can further improve the registration accuracy. The proposed approach is trained in an unsupervised fashion, and no ground-truth
registration fields or landmark annotations are needed. Experimental results
demonstrate that our registration method outperforms current state-of-the-art
algorithms using public available ACDC cardiac cine MRI dataset [25].
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