Self-adaptive deep learning-based segmentation for universal and functional clinical and preclinical CT image analysis.

Journal: Computers in biology and medicine
PMID: 39013341

Abstract

BACKGROUND: Methods to monitor cardiac functioning non-invasively can accelerate preclinical and clinical research into novel treatment options for heart failure. However, manual image analysis of cardiac substructures is resource-intensive and error-prone. While automated methods exist for clinical CT images, translating these to preclinical μCT data is challenging. We employed deep learning to automate the extraction of quantitative data from both CT and μCT images.

Authors

  • Anne-Wietje Zwijnen
    Department of Molecular Genetics, Erasmus University Medical Center, Rotterdam, the Netherlands.
  • Leon Watzema
    Phares/ESP Consultancy, Hank, the Netherlands.
  • Yanto Ridwan
    AMIE Core Facility, Erasmus Medical Center, Rotterdam, the Netherlands.
  • Ingrid van Der Pluijm
    Department of Molecular Genetics, Erasmus University Medical Center, Rotterdam, the Netherlands; Department of Vascular Surgery, Erasmus University Medical Center, Rotterdam, the Netherlands.
  • Ihor Smal
    Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology, Erasmus University Medical Center, Rotterdam, the Netherlands.
  • Jeroen Essers
    Department of Molecular Genetics, Erasmus University Medical Center, Rotterdam, the Netherlands; Department of Vascular Surgery, Erasmus University Medical Center, Rotterdam, the Netherlands; Department of Radiotherapy, Erasmus University Medical Center, Rotterdam, the Netherlands. Electronic address: j.essers@erasmusmc.nl.

Keywords

External Resources

Popular Topics
Recent Journals