SPHERE: From MRI Sampling Mechanisms to Spatial Priors for Generalizable Brain Tumor Segmentation

Brain tumor MRI segmentation is essential for clinical diagnosis and treatment planning. Although deep learning methods have achieved significant progress, most models operate solely in the image domain and overlook the influence of k-space sampling on spatial structure formation. This physical information gap often limits model generalization when encountering varying scanning conditions across different clinical centers. To address this issue, we propose SPHERE, Sampling-Prior Harmonized rEconstruction-aware Representation lEarning, a samplingaware framework for brain tumor MRI segmentation. SPHERE-Recover estimates k-space sampling–related geometric parameters directly from reconstructed MRI and transforms discrete scanning conditions into a continuous deformation vector field (DVF) that represents samplinginduced structural perturbations. Building on this prior, we develop SPHERE-Seg and introduce a Deformation Prior Module (DPM) to inject the DVF into feature modeling, enabling sampling-driven structural consistency learning. Experiments on BraTS and MSD show that SPHERE improves segmentation accuracy, boundary stability, and crossdataset generalization. These results demonstrate that explicitly modeling MRI sampling mechanisms yields robust, generalizable, and efficient brain tumor segmentation.