【讲座题目】Diffeomorphic Models for Image Registration from shallow approaches to deep learning methods
【时 间】2019.07.04(周四) 15：00
【地 点】主楼C636
【主 讲 人】陈柯
【主讲人简介】陈柯(Chen Ke)教授，博士生导师，担任如下职务:利物浦大学数字图像技术研究中心主任(Director of Centre for Mathematical Imaging Techniques)，英国国家工程和自然科学研究委员会利物浦医疗健康数学中心主任(Director of EPSRC Liverpool Centre for Mathematics in Healthcare).皇家御批数学家（Chartered Mathematician）.
【报告内容简介】
Image registration is a core problem in Imaging and Data Sciences. Although many models and computational algorithms have been developed in recent years, it remains a big challenge to achieve both an accurate solution and fast speed for real time applications. In this talk, after reviewing a few classes of models, we discussthe deep learning framework. To offer wider applicability, we consider the scenario where ground-truth deformation fields are not available for training. We propose that the deformation fields are self-trained by a variational model compromised by an image similarity metric and a regularization term. The latter builds in a constraint on the determinant of the transformation in order to obtain a diffeomorphic solution.
The proposed algorithm is first trained and tested on synthetic and real mono-modal images. The results show how it deals with large deformation registration problems and leads to a real time solution with no folding. It is then generalised to multi-modal images. To improve the robustness, we combine the deep learning algorithm with a pre-processing approach. The initial given pair of images, which are non-linearly correlated, are first processed and optimized to serve the purpose of “intensity or edge correction”. This pre-processing step is based on the reproducing Kernel Hilbert space theory and yields intermediate new images which are more strongly correlated than the original ones and which will be used for training the model. Initial experiments and comparisons with learning and non-learning models demonstrate that this approach can deliver good performances and simultaneously generate an accurate diffeomorphic transformation.
Joint work with Dr A. Theljani (Liverpool).
数理学院
2019年7月1日
