Structured Routing over Propagation Axes and Receptive Supports for Volumetric Medical Image Segmentation

Volumetric medical image segmentation requires context that adapts to anatomical direction, spatial location, and semantic class. We introduce VERSA, a structured adaptive routing framework that learns how contextual evidence propagates and which spatial supports inform each prediction. In latent space, V-BAR RWKV learns spatially varying mixtures of bidirectional recurrent responses along three orthogonal axes and integrates them with anisotropic multi-support context. In prediction space, DiVERS constructs nested point, axial, planar-union, and volumetric support experts over decoder features and combines them with boundary-enhanced and pooled-context experts through class- and location-conditioned routing. Normalized expert disagreement regulates the bounded gain of contextual corrections to a point-supported anchor. With 3.67M parameters, VERSA outperforms 19 existing state-of-the-art methods on all 12 public benchmarks spanning CT, MRI, and OCT. Controlled ablations show that learned routing outperforms uniform aggregation with identical candidate axes and support experts, demonstrating the value of adaptive context selection beyond receptive-field expansion.