Decoder-Only Image Registration

In unsupervised medical image registration, the predominant approaches involve the utilization of a encoder-decoder network architecture, allowing for precise prediction of dense, full-resolution displacement fields from given paired images. Despite its widespread use in the literature, we argue for the necessity of making both the encoder and decoder learnable in such an architecture. For this, we propose a novel network architecture, termed LessNet in this paper, which contains only a learnable decoder, while entirely omitting the utilization of a learnable encoder. LessNet substitutes the learnable encoder with simple, handcrafted features, eliminating the need to learn (optimize) network parameters in the encoder altogether. Consequently, this leads to a compact, efficient, and decoder-only architecture for 3D medical image registration. Evaluated on two publicly available brain MRI datasets, we demonstrate that our decoder-only LessNet can effectively and efficiently learn both dense displacement and diffeomorphic deformation fields in 3D. Furthermore, our decoder-only LessNet can achieve comparable registration performance to state-of-the-art methods such as VoxelMorph and TransMorph, while requiring significantly fewer computational resources. Our code and pre-trained models are available at https://github.com/xi-jia/LessNet.