## 5 Discussion  
The results of the presented study demonstrate the efficacy of using deep learning techniques, particularly the U-Net architecture, for the segmentation and 3D modeling of bones in CBCT orthopedic imaging.  
The performances of the proposed U-Net and a benchmark SegNet architecture for CBCT segmentation of bones were compared, employing different training strategies including axial, MPT, and MV. The evaluation on three CBCT volumes revealed that both the U-Net and SegNet architectures achieve high segmentation accuracy, but with distinct differences in computational efficiency and parameter count. U-Net trained with MPT exhibited the highest performance metrics (\(JI=0.939\pm 0.010\), \(DC=0.969\pm 0.005\)), particularly excelling in handling complex anatomical structures. This training strategy effectively leverages information from multiple planes, enhancing segmentation robustness across different orientations. However, while MV also achieved high accuracy, it required significantly more computational resources and time due to the necessity of training and combining three separate networks. Although SegNet provided competitive results regarding the JI and DC, it required a substantially higher number of parameters than U-Net. This higher parameter count translates into increased computational load and longer training times, which may be a limiting factor in resource-constrained environments. The challenges observed in segmenting frontal and sagittal views using axial-trained networks highlight the importance of considering multiplanar information during training. The superior performance of MPT underscores its potential as a preferred training strategy for enhancing the accuracy and reliability of CBCT data segmentation.  
The choice to focus on a simple network with fewer parameters was driven by the need to create a tool that balances technical performance with practical usability. While more advanced models may offer incremental improvements in segmentation accuracy, they often come at the cost of increased complexity and longer inference times. This approach demonstrates that a well-designed, simple architecture can provide excellent results with significant advantages in speed and user experience, making it highly suitable for real-world clinical applications.  
By comparing the Hausdorff distances and the JI and DC between the meshes generated by various segmentation methods, it is evident that the U-Net-MPT-based approach offers superior accuracy. The Hausdorff distance indicates that the U-Net MPT segmentation method yields models with closer alignment to the ground truth, which is critical for applications requiring high precision, such as pre-surgical planning and customized implant design. The workflow's user interface has also proven to be effective in facilitating  
Figure 8: Patient-specific 3D model of a canine femur and pelvis obtained through the proposed workflow.  
the segmentation and visualization process. The intuitive design allows users to generate 3D models easily. The ability to accurately segment and model bones such as the talus, wrist, knee, elbow, and shoulder underscores the versatility of the proposed approach.It allows the generation of an accurate instance segmentation of bones in different anatomical parts without the need to train different neural networks for each specific task.  
The method may struggle with highly complex or irregular anatomical structures, such as bones with extensive deformities or fractures. In such cases, the segmentation accuracy could be reduced, potentially requiring manual intervention to correct the segmentation boundaries.