Anatomy-guided, modality-agnostic segmentation of neuroimaging abnormalities.

ABSTRACT: Magnetic resonance imaging (MRI) offers multiple sequences that provide complementary views of brain anatomy and pathology. However, real-world datasets often exhibit variability in sequence availability due to clinical and logistical constraints. This variability complicates radiological interpretation and limits the generalizability of machine learning models that depend on consistent multimodal input. In this work, we propose an anatomy-guided and modality-agnostic framework for assessing disease-related abnormalities in brain MRI, leveraging structural context to enhance robustness across diverse input configurations. We introduce a novel augmentation strategy, Region ModalMix, which integrates anatomical priors during training to improve model performance when some modalities are abse

SUBMITTER: Lteif D 

PROVIDER: S-EPMC12060938 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature