# A Refined Approach to Segmenting and Quantifying Inter-Fracture Spaces in Facial Bone CT Imaging
## 4 Discussion  
The results of this study demonstrate significant improvements in the segmentation of facial bone fractures through the use of refined ground truth data and a novel segmentation model. The observed increase in the Dice similarity coefficient from 0.33 to 0.67 highlights the impact of using a refined dataset, underscoring the importance of reducing human error in the creation of ground truth data. This finding emphasizes the value of systematic refinement in medical imaging to improve the accuracy of automated models, which ultimately benefits clinical outcomes. One of the key strengths of our approach is the integration of expert annotations with automated refinement using nnUnet [28]. By combining the expertise of clinicians with machine learning techniques, we were able to create a more robust and reliable dataset. This hybrid approach addresses the inherent variability which arises from human annotation, particularly in complex structures like facial bones, where fracture boundaries are often challenging in delineating accurately. This methodology not only improves the accuracy of segmentation but also enhances the generalizability of the model. The use of radiomics analysis to validate the consistency between the unrefined and refined ground truths provides further support for the reliability of our segmentation model. The radiomics features were shown to be consistent in both groups, with the refined ground truth yielding improved performance metrics. This suggests that our refinement process effectively reduces variability, allowing for more precise extraction of radiomics features. This precision is crucial in clinical decision making, as radiomics features can provide additional insights into the severity and characteristics of fractures which may not be apparent through visual assessment alone. We conducted a paired statistical comparison of 107 radiomics features between the Refined GT and Unrefined GT groups using paired  
Figure 6: Comparative visualization of unrefined ground truth (Unrefined GT), refined ground truth (Refined GT), and prediction for facial bone fracture segmentation. The left panel illustrates the unrefined GT with evident inaccuracies and excess segmentation (red), the middle panel displays the refined GT, where excess has been significantly reduced and precision has been enhanced (green), and the right panel shows the prediction, which closely aligns with the refined GT, indicating the high performance of the predictive model (blue). These images underscore the effectiveness of the refinement process in enhancing segmentation accuracy and the model’s capability to replicate refined truth with high fidelity. \(t\)-tests. To adjust for multiple comparisons, we applied Bonferroni correction, leading to a more conservative significance threshold. Of the 107 features analyzed, 82 showed Bonferroni-corrected p values [31] below 0.01, indicating statistically significant differences between the two groups. Overall, Figure 7 effectively highlights how the Refined GT group provided a more comprehensive representation of fracture morphology, capturing the true extent of fractures more accurately than the Unrefined GT group. Figure 8 analyzes the differences in key shape features between the two groups. The web-based tool for evaluating deep learning model performance allowed for a comprehensive analysis of segmentation outcomes. Specifically, by approaching fractures through segmentation, the volume of the space between fractured bones could be quantified, enabling a more objective assessment of fracture severity. The platform provides detailed visualizations of the Unrefined GT, Refined GT, and Predict Mask volumes, allowing users to quantify and compare these values. By sorting the volume ratios in ascending or descending order across all test cases, the tool aids in identifying trends in segmentation performance. This includes detecting over- or under-segmentation tendencies between different ground truths.