Generation of annotated multimodal ground truth datasets for abdominal medical image registration.

ABSTRACT:

Purpose

Sparsity of annotated data is a major limitation in medical image processing tasks such as registration. Registered multimodal image data are essential for the diagnosis of medical conditions and the success of interventional medical procedures. To overcome the shortage of data, we present a method that allows the generation of annotated multimodal 4D datasets.

Methods

We use a CycleGAN network architecture to generate multimodal synthetic data from the 4D extended cardiac-torso (XCAT) phantom and real patient data. Organ masks are provided by the XCAT phantom; therefore, the generated dataset can serve as ground truth for image segmentation and registration. Realistic simulation of respiration and heartbeat is possible within the XCAT framework. To underline the usa

SUBMITTER: Bauer DF 

PROVIDER: S-EPMC8295129 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature