Few-shot learning for deformable image registration in 4DCT images.

ABSTRACT:

Objectives

To develop a rapid and accurate 4D deformable image registration (DIR) approach for online adaptive radiotherapy.

Methods

We propose a deep learning (DL)-based few-shot registration network (FR-Net) to generate deformation vector fields from each respiratory phase to an implicit reference image, thereby mitigating the bias introduced by the selection of reference images. The proposed FR-Net is pretrained with limited unlabeled 4D data and further optimized by maximizing the intensity similarity of one specific four-dimensional computed tomography (4DCT) scan. Because of the learning ability of DL models, the few-shot learning strategy facilitates the generalization of the model to other 4D data sets and the acceleration of the optimization process.

Results

SUBMITTER: Chi W 

PROVIDER: S-EPMC8722248 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature