MedFusionGAN: multimodal medical image fusion using an unsupervised deep generative adversarial network.

ABSTRACT:

Purpose

This study proposed an end-to-end unsupervised medical fusion generative adversarial network, MedFusionGAN, to fuse computed tomography (CT) and high-resolution isotropic 3D T1-Gd Magnetic resonance imaging (MRI) image sequences to generate an image with CT bone structure and MRI soft tissue contrast to improve target delineation and to reduce the radiotherapy planning time.

Methods

We used a publicly available multicenter medical dataset (GLIS-RT, 230 patients) from the Cancer Imaging Archive. To improve the models generalization, we consider different imaging protocols and patients with various brain tumor types, including metastases. The proposed MedFusionGAN consisted of one generator network and one discriminator network trained in an adversarial scenario. Conte

SUBMITTER: Safari M 

PROVIDER: S-EPMC10704723 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature