# A Refined Approach to Segmenting and Quantifying Inter-Fracture Spaces in Facial Bone CT Imaging
## 4 Discussion
For instance, it was observed that non-fracture-related empty spaces in the mandible, possibly missed in the unrefined GT group, were sometimes identified as fractures, demonstrating the utility of the refined GT in correcting such oversights. This is shown in Figure 9.  
Despite these promising results, there are several limitations to our study. First, the segmentation model was trained and validated using data from a single institution, which may limit its generalizability to other clinical settings with different imaging protocols or patient populations. Future studies should include datasets from multiple institutions to enhance the robustness of the model across diverse patient demographics and imaging conditions. Additionally, the current model focuses solely on the segmentation of fracture spaces without differentiating between types of fractures, such as acute versus past fractures. Addressing this limitation will be a key focus of future research, as distinguishing between different types of fractures is crucial for developing more tailored treatment strategies. Figure 7: The Bonferroni-corrected \(p\) values for the 107 shape features, with the \(-\)log10(\(p\)-value) on the y axis. The red dashed line marks the significance threshold (\(p=0.01\)), and features above this line are considered significantly different between the Refined GT and Unrefined GT groups. Notable features such as “Maximum3 DDiameter”, “MajorAxisLength”, and “Maximum2 DDiameterSlice” showed the largest differences, reflecting significant variations in the geometric properties of the fractures between the two groups. Initially, the bounding box regions labeled by medical doctors included deformations caused by old fractures, which were also marked as fractures. Therefore, future research should aim to differentiate clearly between acute fractures, where the fracture line is evident, and deformations which are likely due to old fractures by providing distinct labels for each type. In conclusion, this study presents a significant advancement in the segmentation of facial bone fractures, providing a foundation for improved diagnostic precision and treatment planning. By refining ground truth datasets and leveraging machine learning  
Figure 8: Comparison of Refined GT and Unrefined GT for shape features. One notable observation from the shape feature analysis is that diameter- and length-related features tended to be larger in the Refined GT group compared with the Unrefined GT group. This suggests that during the creation of the Unrefined GT group, clinicians may have inadvertently missed parts of the actual fracture, resulting in underestimation. Even when annotated by experienced labelers, there can be a tendency toward conservative segmentation due to concerns of over-segmentation. This likely resulted in the under-segmentation observed in the Unrefined GT group. Figure 9: Example visualizations from the web-based tool for evaluating deep learning model performance, illustrating trends in segmentation outcomes and identifying empty spaces in the mandible, highlighted with yellow arrows. techniques, we demonstrated that automated segmentation can achieve a high level of accuracy. Future research will aim to address the current limitations and expand the model's capabilities, ultimately contributing to better patient care and outcomes in the management of facial bone fractures. This study has several limitations. First, the segmentation model was developed and validated using data from a single institution, which may limit its generalizability to different clinical settings with varying imaging protocols. Multi-institutional validation is required to enhance the model's robustness. Second, the proposed approach does not distinguish between acute and chronic fractures, which could provide further insights for personalized treatment planning. Additionally, the stepwise annotation process, while improving segmentation accuracy, remains resource-intensive and may pose scalability challenges.