MSB-Net: A Coarse-to-Fine Multi-Scale Boundary-Aware 3D Network for Kidney Tumor Segmentation

Accurate segmentation of kidney tumors is critical for clinical diagnosis and therapeutic planning. However, blurred tumor boundaries often make tumors difficult to distinguish from the surrounding tissues. The large variation in tumor size also leads to diverse shapes and spatial distributions in CT images, which makes reliable lesion localization difficult. To address these challenges, we propose MSB-Net, a unified coarse-to-fine 3D encoder–decoder framework that coordinates localization-oriented skip selection and high-resolution structural refinement within one network. The multiscale attention gate (MSAG) performs decoder-guided filtering on skip connections to reduce irrelevant background responses and improve localization across tumors of different sizes. The structure-aware boundary enhancement module (SBEM) then injects encoder-guided structural cues into the two highest-resolution decoder stages through bounded residual enhancement, without adding an extra boundary branch. In addition, a hybrid loss combines soft Dice, cross-entropy, and boundary-band binary cross-entropy terms, with the boundary term weighted by λbound=0.1. Experiments on KiTS23 and KiPA22 demonstrate consistent quantitative improvements. MSB-Net achieves a mean Dice score of 84.89% on KiTS23, exceeding the strongest baseline by 2.15 percentage points, and tumor Dice and surface Dice scores of 90.13% and 76.98% on KiPA22, respectively.