Comparative analysis of intestinal tumor segmentation in PET CT scans using organ based and whole body deep learning.

Journal: BMC medical imaging
PMID: 39962481

Abstract

BACKGROUND: 18-Fluoro-deoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) is a valuable imaging tool widely used in the management of cancer patients. Deep learning models excel at segmenting highly metabolic tumors but face challenges in regions with complex anatomy and normal cell uptake, such as the gastro-intestinal tract. Despite these challenges, it remains important to achieve accurate segmentation of gastro-intestinal tumors.

Authors

  • Mahsa Torkaman
    Genentech, Inc, South San Francisco, CA, USA. torkaman.mahsa@gene.com.
  • Skander Jemaa
    Genentech, Inc., South San Francisco, CA, USA.
  • Jill Fredrickson
    Genentech, Inc., South San Francisco, CA, USA.
  • Alexandre Fernandez Coimbra
    Genentech, Inc, South San Francisco, CA, USA.
  • Alex de Crespigny
    From the Department of Product Development-Personalized HealthCare Imaging (A.P.K., Z.S., T.B., R.A.D.C.), Clinical Imaging Group, gRED (D.C., A.d.C.), and DevSci OMNI-Biomarker Development (X.J.), Genentech, 600 E Grand Ave, South San Francisco, CA 94080; and Global Product Development Medical Affairs, Neuroscience, F. Hoffmann-La Roche, Basel, Switzerland (L.G.).
  • Richard A D Carano
    From the Department of Product Development-Personalized HealthCare Imaging (A.P.K., Z.S., T.B., R.A.D.C.), Clinical Imaging Group, gRED (D.C., A.d.C.), and DevSci OMNI-Biomarker Development (X.J.), Genentech, 600 E Grand Ave, South San Francisco, CA 94080; and Global Product Development Medical Affairs, Neuroscience, F. Hoffmann-La Roche, Basel, Switzerland (L.G.).

Keywords

External Resources

Popular Topics
Recent Journals