# Automatic Needle Route Proposal in Preoperative Neck CT for Injection Laryngoplasty
## 3 Experimental Results
### Needle Route Accuracy  
Finally, we present the expert evaluation results for the computed needle routes in Table 5. We also present the volumetric renders of some example needle routes in different CT volumes in Figure 5. As mentioned earlier, a result is considered accurate only if it indicates a safe insertion path practically. A route with even a slight risk was labeled as _inaccurate_. The intensity sum-based cost function gave 84.19% and 84.93% accuracy with respect to observers 1 and 2, respectively. The proposed maximum intensity cost resulted in a further improvement (\(p\)-value \(<\) 0.05) showing 88.97% and 89.71%, respectively. Excluding the cases where the vocal fold localization was inaccurate, the needle route accuracy was 95.64% and 95.31%, respectively. The needle projection accuracy was considerably high, given that the inter-observer agreement was 95.58%. Figure 4: Two examples (in top and bottom) of improved segmentation based on the proposed geodesic distance. Yellow region indicates the intersection of the resultant mask and the ground truth (i.e., true positive region). Green region refers to the over-segmented region (i.e., false positive region). Red region indicates the under-segmented region (i.e., false negative region). Figure 5: Optimal needle routes by the proposed automatic method. The orange and blue lines in the render represent the route to the right and left vocal folds, respectively. The needle insertion lines are extended by 40 voxels beyond the skin for visualization purposes. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**Observer 1**} & \multicolumn{2}{c}{**Observer 2**} \\
**Needle Route** & **Intensity Sum** & **Max Intensity** & **Intensity Sum** & **Max Intensity** \\  & **Cost** & **Cost** & **Cost** & **Cost** \\ \hline Right & 86.03\% & **90.44\%** & 87.50\% & **91.18\%** \\ Left & 82.35\% & **87.50\%** & 82.35\% & **88.23\%** \\ Right * & 91.40\% & **96.09\%** & 91.55\% & **95.38\%** \\ Left * & 89.60\% & **95.20\%** & 90.48\% & **95.24\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Needle route accuracy. Figure 5 presents volumetric renders of the automated needle routes for different cases. The maximum intensity cost minimization could successfully avoid the thyroid and cricoid cartilages, placing the needle insertion points in the desired cricothyroid membrane. Moreover, the insertion points were also in the desired range laterally because our search space strictly followed the medically implied range after computing the midpoint. From this desired area, the proposed routes could reach the vocal folds without facing any obstacles. The entire process from vocal fold localization up to the optimal needle route search took about 9 s (as tested on a 3.60 GHz CPU and RTX 3080 GPU). Therefore, the proposed method can potentially be useful for guiding and accelerating preoperative planning. The proposed method was robust under noisy CT volumes. The needle routes mainly relied upon airway segmentation and the minimal intensity path calculation. The airway segmentation was not affected by noise because the air has a clearly different intensity with a large margin of approximately 900 HU compared with other regions (fat, water, soft tissue, etc.). Moreover, the intensity of the thyroid cartilage is also high with a large difference (about 400 HU) from the other regions in the larynx. Therefore, the paths passing through cartilages were always avoided because the paths heading to the desired cricothyroid membrane constantly had minimal intensity even under the noise. In Figure 6, we present such a noisy case with a relatively low signal-to-noise ratio (SNR) of 7.27 dB whereas the average SNR was 9.42 dB. As mentioned earlier, no automatic needle trajectory planning work for TIL currently exists. Therefore, we could not directly compare our method to the existing works. A wide range of computational needle trajectory studies focused on liver tumor thermal ablation [16; 17; 18; 19].