3D Deep Learning for Anatomical Structure Segmentation in Multiple Imaging Modalities.

ABSTRACT: Accurate, quantitative segmentation of anatomical structures in radiological scans, such as Magnetic Resonance Imaging (MRI) and Computer Tomography (CT), can produce significant biomarkers and can be integrated into computer-aided assisted diagnosis (CADx) systems to support the interpretation of medical images from multi-protocol scanners. However, there are serious challenges towards developing robust automated segmentation techniques, including high variations in anatomical structure and size, the presence of edge-based artefacts, and heavy un-controlled breathing that can produce blurred motion-based artefacts. This paper presents a novel computing approach for automatic organ and muscle segmentation in medical images from multiple modalities by harnessing the advantages of deep learn

SUBMITTER: Villarini B 

PROVIDER: S-EPMC8867534 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature