# A Refined Approach to Segmenting and Quantifying Inter-Fracture Spaces in Facial Bone CT Imaging
## 5 Conclusions  
In this study, we introduced a novel segmentation model for the spaces between fractured facial bones using CT images, addressing a critical gap in the accurate diagnosis of and treatment planning for facial bone fractures. The proposed segmentation approach leveraged both unrefined and refined ground truth datasets, with significant improvements in segmentation performance, as evidenced by an increase in the Dice similarity coefficient from 0.33 to 0.67. The automated segmentation model demonstrated the ability to accurately delineate fracture regions, enabling the precise analysis of a fracture's severity and volume. This advancement is of particular importance for clinical practice, where detailed and quantifiable fracture information can support personalized treatment plans and improve outcomes. Our radiomics analysis further validated the consistency between the unrefined and refined ground truth groups, highlighting the enhanced reliability and accuracy achieved through refinement. The developed web-based tool also provides a valuable platform for clinical evaluation, allowing for a detailed examination and comparison of segmentation results in both 2D and 3D contexts.  
The main contributions of this study can be summarized as follows. First, we developed a refined segmentation approach by implementing a stepwise annotation process, combining expert-driven initial labeling with automated refinement using the nnU-Net framework. This process significantly improved the segmentation accuracy and reliability. Second, our study introduced a quantitative analysis of inter-fracture spaces, providing objective assessments which are crucial for clinical decision making. Third, radiomics-based validation was conducted to confirm the improved consistency and reduced variability of the refined ground truth. Additionally, we developed a web-based visualization tool to facilitate easy comparison of segmentation results, improving accessibility and usability for clinicians. These contributions collectively demonstrate the potential for integrating automated segmentation into clinical workflows to enhance diagnostic precision and treatment planning.  
Overall, our work presents a promising tool for enhancing diagnostic precision and treatment planning for facial bone fractures. Future work will focus on extending the model to differentiate between acute and past fractures and further optimizing the segmentation process to improve efficiency and accuracy.  
**Author Contributions:** In this study, [PERSON] designed the method, performed the statistical analysis, and drafted the manuscript. [PERSON] wrote the code and trained the models. [PERSON]-[PERSON] analyzed the radiomics features. [PERSON] and [PERSON] improved the manuscript. [PERSON] and [PERSON]-[PERSON] gained IRB approval and collected and selected the data. [PERSON] processed the dataset, including initial annotation for all data. [PERSON] and [PERSON] coordinated and supervised the whole work. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Global Innovation Special Zone Innovation Project (RS-2024-00488398) of the Ministry of SMEs and Startups.  
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Kangwon National University Hospital (protocol code KNUH-2023-11-006-001).  
Not applicable.  
Regarding code availability, the code is available upon request from the corresponding author. The data presented in this study are available upon request from the corresponding author.  
We would like to acknowledge all the members of the SEOREU Corp. who contributed to producing the segmentation data used in this study.  
[PERSON] is founder and CEO of ZIOVISION. [PERSON] is the COO of ZIOVISION. [PERSON], [PERSON], and [PERSON] owns stock options in ZIOVISION. The other authors have no conflicts of interest to declare.