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

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.