# Automated Traumatic bleeding Detection in Whole-Body CT Using 3D Object Detection Model
## 4 Discussion  
We developed an automated method for detecting traumatic bleeding in WBCT images using a 3D object detection model enhanced with an ASPP module. The proposed CAD software achieved a sensitivity of 70.0% with 46.9 FPs per case. Across all sensitivity levels, the proposed method, incorporating the ASPP module, outperformed our previous method [18]. Replacing the segmentation-based 3D U-Net# with a 3D object detection model (without the ASPP module) improved the maximum detection sensitivity but did not reduce the number of FPs per case at sensitivity below 80%. In contrast, incorporating the ASPP module further improved the maximum sensitivity and reduced the number of FPs per case at a sensitivity below 80%. By applying atrous convolutions with varying dilation rates, the module enabled multi-resolution analysis of high-level semantic features, which proved effective for detecting bleeding regions of different sizes and shapes. Positioned at the end of the encoder, the ASPP module leverages high-level semantic features to aggregate contextual information across multiple scales. Consequently, the proposed method maintains or improves sensitivity and significantly reduces the number of FPs per case, particularly at higher sensitivity thresholds, thereby increasing the clinical viability compared to that of our previous method. Furthermore, the non-overlapping 95% CIs for the number of FPs per case suggest that the observed reductions are statistically meaningful. Despite these improvements, some FPs persist. One contributing factor is artifacts caused by artificial objects or motion, as shown in Figure 7a. Another major cause of FPs is thin vessels (Figure 7b), which appear as tubular structures with high CT values and are difficult to distinguish from bleeding. The variability in contrast enhancement across scans further complicates the accurate extraction of these thin vessels. Although region-growing is employed during post-processing in multi-organ segmentation to extract thin vessels, accurate segmentation remains challenging. A potential approach to address these persistent FPs is to incorporate a classification step using a 3D convolutional neural network that analyzes the VOIs containing the lesion candidate after the FP reduction stage. A characteristic output pattern observed in the object detection model is the clustering of multiple small bounding boxes, as shown in Figure 7b. This clustering appears not only around the true bleeding regions but also in regions without bleeding, suggesting that such patterns contribute to the remaining FPs. Although small-region removal and NMS  
Figure 7: Examples of remaining FPs after FP reduction (detection model: with the ASPP module): (**a**) FPs in streak artifacts due to an artificial object, and (**b**) FPs in thin vessels. 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. Orange arrows indicate thin vessels. were applied as part of the FP reduction process, this fragmentation persisted, indicating that the current post-processing pipeline has limited effectiveness in eliminating such clustered FPs. To further reduce this redundancy, additional strategies, such as confidence-aware merging or spatial clustering, could be explored to combine overlapping predictions more effectively. As our method shows a relatively high number of FPs per case in high-sensitivity settings, this may raise concerns regarding its clinical use. However, in practical CAD software, it is common to adjust the number of findings presented to physicians by applying confidence-based thresholds to balance sensitivity and the number of FPs. This approach has been widely adopted in various detection tasks [30; 31]. The proposed method reduced the processing time to 4.2 \(\pm\) 1.1 min, shortening it by approximately 2.2 min compared to the previous method [18].