Label-efficient sequential model-based weakly supervised intracranial hemorrhage segmentation in low-data non-contrast CT imaging.

Journal: Medical physics
PMID: 39962740

Abstract

BACKGROUND: In clinical settings, intracranial hemorrhages (ICH) are routinely diagnosed using non-contrast CT (NCCT) in emergency stroke imaging for severity assessment. However, compared to magnetic resonance imaging (MRI), ICH shows low contrast and poor signal-to-noise ratio on NCCT images. Accurate automated segmentation of ICH lesions using deep learning methods typically requires a large number of voxelwise annotated data with sufficient diversity to capture ICH characteristics.

Authors

  • Shreyas H Ramananda
    Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, Karnataka, India.
  • Vaanathi Sundaresan
    Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Oxford-Nottingham Centre for Doctoral Training in Biomedical Imaging, University of Oxford, UK; Oxford India Centre for Sustainable Development, Somerville College, University of Oxford, UK. Electronic address: vaanathi.sundaresan@dtc.ox.ac.uk.

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