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 a consistent multimodal input. Here, we propose an anatomy-guided, modality-agnostic framework to assess disease-related abnormalities in brain MRI, leveraging structural context to ensure robustness in diverse input configurations. Central to our approach is Region ModalMix (RMM), an augmentation strategy that integrates anatomical priors during training to improve model performance under missing or variable modality

SUBMITTER: Lteif D 

PROVIDER: S-EPMC12441204 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature