SHIVA-CMB: a deep-learning-based robust cerebral microbleed segmentation tool trained on multi-source T2*GRE- and susceptibility-weighted MRI.

ABSTRACT: Cerebral microbleeds (CMB) represent a feature of cerebral small vessel disease (cSVD), a prominent vascular contributor to age-related cognitive decline, dementia, and stroke. They are visible as spherical hypointense signals on T2*- or susceptibility-weighted magnetic resonance imaging (MRI) sequences. An increasing number of automated CMB detection methods being proposed are based on supervised deep learning (DL). Yet, the lack of open sharing of pre-trained models hampers the practical application and evaluation of these methods beyond specific data sources used in each study. Here, we present the SHIVA-CMB detector, a 3D Unet-based tool trained on 450 scans taken from seven acquisitions in six different cohort studies that included both T2*- and susceptibility-weighted MRI. In a held-

SUBMITTER: Tsuchida A 

PROVIDER: S-EPMC11680838 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature