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

ZHANG Yinhui; LIU Kai; HE Zifen; ZHANG Jinkai; CHEN Guangchen; MA Zhijian

doi:10.11999/JEIT250732

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ZHANG Yinhui, LIU Kai, HE Zifen, ZHANG Jinkai, CHEN Guangchen, MA Zhijian. Dynamic Wavelet Multi-Directional Perception and Geometry Axis-Solution Guided 3D CT Fracture Image Segmentation[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250732

Citation:

ZHANG Yinhui, LIU Kai, HE Zifen, ZHANG Jinkai, CHEN Guangchen, MA Zhijian. Dynamic Wavelet Multi-Directional Perception and Geometry Axis-Solution Guided 3D CT Fracture Image Segmentation[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250732

Citation:

ZHANG Yinhui, LIU Kai, HE Zifen, ZHANG Jinkai, CHEN Guangchen, MA Zhijian. Dynamic Wavelet Multi-Directional Perception and Geometry Axis-Solution Guided 3D CT Fracture Image Segmentation[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250732

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

doi: 10.11999/JEIT250732 cstr: 32379.14.JEIT250732

1.
Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China
2.
Department of Sports Medicine, The First People’s Hospital of Kunming, Kunming 650224, China

Funds: The National Natural Science Foundation of China (62561032, 62171206, 62061022), Joint Special Project of Yunnan Provincial Department of Science and Technology and Kunming Medical University – ‘Digital Mechanics Simulation for Optimizing Internal Fixation of Ankle Fractures’ (202001AY070001-094), Kunming Municipal Health Commission Ten-Hundred-Thousand Talent Project (2021-SW(Reserve)-28), Kunming Municipal Health Commission Medical Technology Center Project (2022-SW(Tech)-15)

Accepted Date: 2025-12-12
Rev Recd Date: 2025-12-12

Available Online: 2025-12-19

Abstract

Abstract

Objective Accurate segmentation of fracture surfaces in 3D CT images is critical for orthopedic surgical planning, particularly in determining optimal nail insertion angles perpendicular to fracture planes. However, existing methods exhibit three key limitations: insufficient capture of deep global volumetric context, directional texture ambiguity in low-contrast fracture regions, and underutilization of geometric features decoding. To overcome these challenges, we propose DWAG-Net, a novel framework that integrates Dynamic Wavelet Perception and Geometry-Axis Guidance, to significantly enhance segmentation precision for complex tibial fractures and provide reliable 3D digital guidance for preoperative planning. Methods The architecture extends 3D nnU-Netv2 with three core innovations. First, the Dynamic Multi-View Aggregation (DMVA) module dynamically fuses tri-planar (axial/sagittal/coronal) and full-volume features via learnable parameter interpolation (optimized kernel size: 2×2×2) and channel-wise Hadamard product, enhancing global context modeling. Second, the Wavelet Direction Perception Enhancement (WDPE) module decomposes inputs using 3D Symlets discrete wavelet transform and applies direction-specific enhancement to eight subbands: adaptive convolutional kernels (e.g., [5,3,3] for depth-dominant fractures) amplify texture details in high-frequency subbands, while cross-subband fusion strategies integrate complementary features. Third, the Geometry Axis-Solution Guided (GASG) module embeds in decoders to enforce anatomical consistency by computing axis-level affinity maps (depth/height/width) that combine geometric similarity with spatial distance decay, and by optimizing boundary delineation through rotational positional encoding and multi-axis attention. The model was trained on the YN-TFS dataset (110 tibial fracture CT scans, resolution 0.39–1.00 mm) using SGD optimizer (lr=0.01, momentum=0.99) and a class-weighted loss (background:0.5, bone:1, fracture:5) to mitigate severe pixel imbalance. Results and Discussions DWAG-Net achieved state-of-the-art performance, with a mean Dice score of 71.20% (Table 1), surpassing nnU-Netv2 by 5.06% (fracture surface Dice: 69.48%, +7.12%). Boundary precision improved significantly, yielding a mean HD95 of 1.38 mm (fracture surface: 1.54 mm, –3.70 mm). Ablation studies (Table 2) confirmed each module’s contribution: DMVA improved Dice by 2.40% through adaptive multi-view fusion; WDPE resolved directional ambiguity, adding a 5.84% fracture-surface Dice gain; GASG provided a further 1.20% gain by enforcing geometric consistency. Key configurations included optimal DMVA parameters (2×2×2), Symlets wavelets, and sequential axis processing (D→H→W). Qualitatively, DWAG-Net preserved fracture integrity where U-Mamba/nnWNet failed and reduced over-segmentation compared with nnFormer/UNETR++ (Fig. 4). Conclusions DWAG-Net establishes a new state-of-the-art for 3D fracture segmentation by synergizing multi-directional wavelet perception and geometric guidance. Its key innovations—DMVA for volumetric context fusion, WDPE for directional texture enhancement, and GASG for anatomically consistent decoding—deliver clinically critical precision (71.20% Dice, 1.38 mm HD95) and enable data-driven surgical planning. Future work will focus on optimizing loss functions for severe class imbalance.
- 3D fracture segmentation,
- Tibial CT images,
- Dynamic multi-view,
- Wavelet perception,
- Geometric guidance

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References(28)

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