# Automated Traumatic bleeding Detection in Whole-Body CT Using 3D Object Detection Model
## 3 Results  
Figure 5 compares the FROC curves of our previous method [19] with those of the proposed methods. The 3D object detection model without the ASPP module achieved a maximum sensitivity of 85.8% at 567.5 FPs per case. Incorporating the ASPP module further improved the maximum sensitivity to 90.0%, with 587.3 FPs per case. In contrast, the previous method achieved a maximum sensitivity of 83.7% at 598.4 FPs per case. Table 1 summarizes the number of FPs per case at various sensitivity levels. For the proposed method without the ASPP module, the numbers of FPs per case at sensitivities of 60.0% and 70.0% were 31.7 (95% CI: 31.3-32.0) and 57.8 (95% CI: 57.1-58.5), respectively. With the ASPP module, these values improved to 24.3 (95% CI: 24.0-24.6) and 46.9 (95% CI: 46.4-47.4), respectively. As shown in Figure 5 and Table 1, the proposed method with the ASPP module consistently outperformed our previous method by reducing the number of FPs per case across all sensitivity levels. Moreover, the non-overlapping 95% CIs suggest a potentially meaningful difference between the methods.  
Figure 4: Design of six-fold cross-validation.  
Figure 5: Average free-response receiver operating characteristic (FROC) curves with 95% confidence intervals along the sensitivity axis (semi-transparent) based on 1000 bootstrap replicates.  
Table 2 compares the processing times of the previous and proposed methods using the ASPP module. The proposed method required 4.0 \(\pm\) 1.0 min, reducing the processing time by 2.2 min compared to the previous method, which took 6.2 \(\pm\) 2.0 min.  
Figure 6 presents two examples comparing detection results with and without the ASPP module. These examples demonstrate successful bleeding detection, regardless of the presence or absence of the ASPP module. However, when comparing the cases with and without the ASPP module, FPs are more effectively reduced when the ASPP module is incorporated. Figure 7 shows examples of the remaining FPs after FP reduction. Figure 7a shows FPs in streak artifacts caused by an artificial object, while Figure 7b presents FPs present in thin vessels. In Figure 7b, multiple clusters are observed, each consisting of several small bounding boxes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Sensitivity [\%]**} \\ \cline{2-4}  & \multicolumn{1}{c}{**60.0**} & **70.0** \\ \hline Previous method [18] & & 29.1 (28.8–29.4) & 48.8 (48.2–49.4) \\ Proposed method & 3D object detection + ASPP module & 31.7 (31.3–32.0) & 57.8 (57.1–58.5) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the number of false positives (FPs) per case between our previous method and the proposed methods. Values in parentheses indicate 95% confidence intervals.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Preprocessing+**} & \multirow{2}{*}{**Bleeding Detection +**} & \multirow{2}{*}{**Total**} \\  & \multicolumn{1}{c}{**Segmentation**} & & \\ \hline Previous method [18] & 2.0 \(\pm\) 0.6 & 4.2 \(\pm\) 1.4 & 6.2 \(\pm\) 2.0 \\ Proposed methods & 2.0 \(\pm\) 0.6 & 2.0 \(\pm\) 0.5 & 4.0 \(\pm\) 1.1 \\ (with ASPP module) & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of inference computation time (min) between the previous method and the proposed methods. Data are mean \(\pm\) standard deviation.  
Figure 6: Examples of detection results: (**a**) bleeding near pelvic bone fracture and (**b**) bleeding in the adrenal gland. All images show the axial section. Green boxes indicate areas of bleeding (ground truth, GT), yellow boxes indicate areas of TPs, and red boxes indicate areas of FPs.