# A Refined Approach to Segmenting and Quantifying Inter-Fracture Spaces in Facial Bone CT Imaging
## 3 Results
### Radiomics Analysis  
The radiomics features were extracted using Python 3.11, with version 3.0.1 of a the pyyradiomics library. Radiomic feature extraction was performed using the pyradiomics library with the following parameters: bin width set to 25, no resampling (resampled-PixelSpacing set to none), B-spline interpolation, verbose output enabled, and 3D feature extraction allowed (force2D set to false). The feature classes enabled for extraction included the first-order statistics (18 features), gray-level co-occurrence matrix (GLCM) (24 features), gray-level run-length matrix (GLRLM) (16 features), gray-level size zone matrix (GLSZM) (16 features), neighboring gray-tone difference matrix (NGTDM) (5 features), gray-level dependence matrix (GLDM) (14 features), and shape features (16 features). For each case, the corresponding CT image and mask were loaded. The pyradiomics extractor was then applied to the CT image and the processed mask to calculate the desired radiomics features. This set-up ensured a systematic and reproducible extraction of radiomic features in all cases, providing a comprehensive dataset for subsequent analysis. The overall differences between the Refined GT and Unrefined GT for each feature are shown in Figure 4. Among them, shape-related features are detailed in Table 2, providing statistical summaries (mean, standard deviation, minimum, quartiles, and maximum) for key shape metrics such as the axis lengths and 2D and 3D diameters.  
\begin{table}
\begin{tabular}{l l c c c c c c c} \hline \hline
**Feature** & **Type** & **Mean** & **Std** & **Min** & **25\%** & **50\%** & **75\%** & **Max** \\ \hline \multirow{2}{*}{LeastAxisLength} & Refined & 19.19 & 13.84 & 0 & 9.25 & 15.36 & 25.86 & 113.25 \\ \cline{2-8}  & Unrefined & 9.79 & 8.73 & 0 & 3.78 & 7.38 & 12.10 & 50.64 \\ \hline \multirow{2}{*}{MajorAxisLength} & Refined & 91.45 & 48.91 & 1.26 & 57.99 & 81.66 & 121.15 & 312.85 \\ \cline{2-8}  & Unrefined & 43.63 & 37.77 & 1.61 & 19.80 & 28.34 & 60.01 & 234.24 \\ \hline \multirow{2}{*}{Maximum2 DDiameterColumn} & Refined & 52.80 & 39.87 & 2.03 & 18.20 & 40.98 & 84.51 & 184.66 \\ \cline{2-8}  & Unrefined & 26.58 & 23.83 & 2.03 & 12.50 & 19.26 & 30.13 & 157.80 \\ \hline \multirow{2}{*}{Maximum2 DDiameterRow} & Refined & 41.01 & 37.01 & 2.04 & 12.12 & 32.01 & 51.35 & 248.91 \\ \cline{2-8}  & Unrefined & 22.99 & 18.62 & 2.04 & 10.60 & 17.39 & 29.52 & 130.60 \\ \hline \multirow{2}{*}{Maximum2 DDiameterSlice} & Refined & 59.89 & 38.60 & 1.16 & 21.56 & 56.63 & 88.56 & 162.65 \\ \cline{2-8}  & Unrefined & 27.04 & 23.47 & 1.55 & 12.60 & 20.00 & 32.96 & 142.92 \\ \hline \multirow{2}{*}{Maximum3 DDiameter} & Refined & 98.93 & 39.73 & 2.20 & 69.22 & 103.47 & 128.03 & 250.44 \\ \cline{2-8}  & Unrefined & 39.53 & 30.63 & 2.36 & 19.02 & 27.30 & 54.04 & 161.32 \\ \hline \multirow{2}{*}{MinorAxisLength} & Refined & 41.96 & 26.43 & 0.59 & 21.69 & 37.62 & 56.16 & 190.24 \\ \cline{2-8}  & Unrefined & 20.21 & 17.12 & 0.59 & 8.95 & 15.80 & 24.85 & 132.67 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of shape-related features between Refined GT and Unrefined GT. The table presents statistical summaries (mean, standard deviation, minimum, quartiles, and maximum) for key shape features, including axis lengths and 2D and 3D diameters.  
Figure 4: Box plot illustrating the normalized radiomics feature values extracted from the refined and unrefined ground truth datasets. Radiomics feature extraction was performed to facilitate a comparative analysis between the refined and unrefined segmentations, highlighting the variability in feature values across different segmentation approaches. Normalization was applied to allow for consistent comparison across features with varying ranges. Of the 107 total features, only the 70 features with Bonferroni-corrected \(p\) values greater than 0.01 are displayed.