Review of Generative Adversarial Networks in mono- and cross-modal biomedical image registration.

ABSTRACT: Biomedical image registration refers to aligning corresponding anatomical structures among different images, which is critical to many tasks, such as brain atlas building, tumor growth monitoring, and image fusion-based medical diagnosis. However, high-throughput biomedical image registration remains challenging due to inherent variations in the intensity, texture, and anatomy resulting from different imaging modalities, different sample preparation methods, or different developmental stages of the imaged subject. Recently, Generative Adversarial Networks (GAN) have attracted increasing interest in both mono- and cross-modal biomedical image registrations due to their special ability to eliminate the modal variance and their adversarial training strategy. This paper provides a comprehensiv

SUBMITTER: Han T 

PROVIDER: S-EPMC9724825 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature