Dynamic Wavelet Multi-Directional Perception and Geometry Axis-Solution Guided 3D CT Fracture Image Segmentation

ObjectiveAccurate segmentation of fracture surfaces in three-dimensional computed tomography (3D CT) images is essential for orthopedic surgical planning, particularly for determining nail insertion angles perpendicular to fracture planes. However, existing approaches present three major limitations: limited capture of deep global volumetric context, directional texture ambiguity in low-contrast fracture regions, and insufficient decoding of geometric features. To address these limitations, a Dynamic Wavelet Multi-Directional Perception and Geometry Axis-Solution Guided Network (DWAG-Net) is proposed to improve segmentation accuracy for complex tibial fractures and to provide reliable 3D digital guidance for preoperative planning.MethodsThe proposed architecture extends 3D nnU-Netv2 through three core components. First, a Dynamic Multi-View Aggregation (DMVA) module adaptively fuses tri-planar views (axial, sagittal, and coronal) with full-volume features using learnable parameter interpolation with an optimized kernel size of 2×2×2 and a channel-wise Hadamard product, thereby strengthening global context representation. Second, a Wavelet Direction Perception Enhancement (WDPE) module applies a 3D Symlets discrete wavelet transform to decompose inputs into eight subbands, followed by direction-specific enhancement. Adaptive convolutional kernels (e.g., [5, 3, 3] for depth-dominant fractures), reinforce texture information in high-frequency subbands, whereas cross-subband fusion integrates complementary features. Third, a Geometry Axis-Solution Guided (GASG) module is embedded in the decoder to maintain anatomical consistency by constructing axis-level affinity maps along depth, height, and width that combine geometric similarity with spatial distance decay, and by refining boundary delineation using rotational positional encoding and multi-axis attention. The network is trained on the YN-TFS dataset, which contains 110 tibial fracture CT scans with spatial resolutions ranging from 0.39 to 1.00 mm. Stochastic gradient descent is used with a learning rate of 0.01 and a momentum of 0.99. A class-weighted loss function with weights of 0.5 for background, 1 for bone, and 5 for fracture is adopted to address severe pixel imbalance.Results and DiscussionsDWAG-Net achieves state-of-the-art performance, with a mean Dice score of 71.20% (Table 1), exceeding that of nnU-Netv2 by 5.06%. For fracture surfaces, the Dice score reaches 69.48%, corresponding to an improvement of 7.12%. Boundary accuracy improves significantly, with a mean 95th percentile Hausdorff distance (HD95) of 1.38 mm and a fracture surface HD95 of 1.54 mm, representing a reduction of 3.70 mm. Ablation studies (Table 2) confirm the contribution of each component. DMVA increases the Dice score by 2.40% through adaptive multi-view fusion. WDPE reduces directional ambiguity and yields a 5.84% gain in fracture surface Dice. GASG provides an additional 1.20% improvement by enforcing geometric consistency. Optimal performance is obtained with a DMVA kernel size of 2×2×2, the use of Symlets wavelets, and sequential axis processing in the order of depth, height, and width. Qualitative comparisons indicate that DWAG-Net preserves fracture continuity in cases where U-Mamba and nnWNet fail, and reduces over-segmentation relative to nnFormer and UNETR++. (Fig. 5).ConclusionsDWAG-Net establishes a state-of-the-art framework for 3D fracture segmentation by integrating multi-directional wavelet perception with geometry-guided decoding. The coordinated use of DMVA, directional texture enhancement, and geometry axis-solution guidance achieves clinically relevant precision, with a Dice score of 71.20% and an HD95 of 1.38 mm. These results support accurate data-driven surgical planning. Future work will focus on refining loss design to further mitigate severe class imbalance.