- Keywords: modality-invariant, multi-modal image registration, mutual information, Parzen window, B-splines, diffeomorphic, deep learning
- TL;DR: Diffeomorphic modality-invariant deep learning image registration using a differentiable mutual information and a B-spline free form deformation (FFD) parameterisation of Stationary Velocity Field (SVF)
- Abstract: We present a deep learning (DL) registration framework for fast mono-modal and multi-modal image registration using differentiable mutual information and diffeomorphic B-spline free-form deformation (FFD). Deep learning registration has been shown to achieve competitive accuracy and significant speedups from traditional iterative registration methods. In this paper, we propose to use a B-spline FFD parameterisation of Stationary Velocity Field (SVF) to in DL registration in order to achieve smooth diffeomorphic deformation while being computationally-efficient. In contrast to most DL registration methods which use intensity similarity metrics that assume linear intensity relationship, we apply a differentiable variant of a classic similarity metric, mutual information, to achieve robust mono-modal and multi-modal registration. We carefully evaluated our proposed framework on mono- and multi-modal registration using 3D brain MR images and 2D cardiac MR images.
- Registration: I acknowledge that publication of this at MIDL and in the proceedings requires at least one of the authors to register and present the work during the conference.
- Authorship: I confirm that I am the author of this work and that it has not been submitted to another publication before.
- Paper Type: both
- Primary Subject Area: Image Registration
- Secondary Subject Area: Image Registration
- Source Code Url: https://github.com/qiuhuaqi/midir
- Data Set Url: https://camcan-archive.mrc-cbu.cam.ac.uk/dataaccess/, https://www.ukbiobank.ac.uk/
- Source Latex: zip