Health AI Central
Peer-Reviewed Publication
Med Phys2025;52(4):2123-2144.April 1, 2025Journal Article

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

Shreyas H Ramananda1, Vaanathi Sundaresan1
1Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, Karnataka, India.
PubMed DOI

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 nu…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free AccountSign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.