Multi-Modal Deformable Image Registration Using Untrained Neural Networks

Quang Luong Nhat Nguyen, Ruiming Cao, Laura Waller

Abstract: Image registration techniques usually assume that the images to be registered are of a certain type (e.g. single- vs. multi-modal, 2D vs. 3D, rigid vs. deformable) and there lacks a general method that can work for data under all conditions. We propose a registration method that utilizes neural networks for image representation. Our method uses untrained networks with limited representation capacity as an implicit prior to guide for a good registration. Unlike previous approaches that are specialized for specific data types, our method handles both rigid and non-rigid, as well as single-and multi-modal registration, without requiring changes to the model or objective function. We have performed a comprehensive evaluation study using a variety of datasets and demonstrated promising performance.11Code: https://github.com/quang-nguyenln/mdirunn.

External IDs:dblp:conf/isbi/NguyenCW25