Towards fully automated segmentation of rat cardiac MRI by leveraging deep learning frameworks.

ABSTRACT: Automated segmentation of human cardiac magnetic resonance datasets has been steadily improving during recent years. Similar applications would be highly useful to improve and speed up the studies of cardiac function in rodents in the preclinical context. However, the transfer of such segmentation methods to the preclinical research is compounded by the limited number of datasets and lower image resolution. In this paper we present a successful application of deep architectures 3D cardiac segmentation for rats in preclinical contexts which to our knowledge has not yet been reported. We developed segmentation models that expand on the standard U-Net architecture and evaluated models separately trained for systole and diastole phases (2MSA) and a single model trained for all phases (1MSA). F

SUBMITTER: Fernandez-Llaneza D 

PROVIDER: S-EPMC9163082 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature