V²-Former: Towards volumetric framework for instance-level segmentation and prediction of fetal ventriculomegaly in anisotropic MRI.

Abstract

Accurate identification and assessment of fetal ventriculomegaly (VM) is crucial for prenatal care. However, conventional diagnosis relies on manual 2D slice-based measurements, which may overlook 3D morphological cues. Existing deep learning approaches typically separate volumetric segmentation from clinical decision-making or rely on global case-level predictions, and rarely encode the clinical workflow of combining measurements with contextual findings. Furthermore, they face significant challenges in capturing the non-uniform clinical relevance and adapting to the anisotropic characteristics of fetal MRI. To address these issues, we introduce V²-Former, a Volumetric Ventricular analysis framework that achieves both ventricle-specific prediction consistent with clinical practice and comprehensive volumetric assessment. Leveraging the query-based transformer paradigm, our method integrates two complementary components: (1) an Anisotropy-Aware Module (AAM) that recalibrates volumetric features to highlight non-uniform diagnostically relevant regions in anisotropic data, and (2) a Ventricular Diagnosis Enhancement (VDE) strategy that encodes diagnostic priors to guide query-based learning for ventricle-specific prediction. Evaluated on a real-world clinical dataset of 384 fetal MRI scans, V²-Former achieves the strongest overall combined performance among the compared methods, providing clinicians with the first end-to-end solution that delivers both ventricle-specific predictions and volumetric evaluations to support clinical VM assessment.