# Automated Traumatic bleeding Detection in Whole-Body CT Using 3D Object Detection Model
## 2 Materials and Methods
### Detection Method Used in This Study
#### 2.2.2 Bleeding Detection  
We used three types of normalized images, following the approach described in our previous studies [16; 18]. The normalization ranges were set to [0, 160], [160; 320], and [320; 450] Hounsfield Units (HU), respectively. After normalization, all voxel values were scaled to the range of [0, 1]. Figure 2 shows the architecture of the 3D object detection model used in this study, which comprises two main components: the backbone and the detection head. This model was inspired by DeepAneDet [20], originally proposed for the detection of cerebral aneurysms. In our setting, the model takes a 3 \(\times\) 96 \(\times\) 96 \(\times\) 96 cubic volume of interest (VOI) and partitions it into a 12 \(\times\) 12 \(\times\) 12 grid of spatial cells. Each cell is responsible for predicting the presence and location of bleeding centered within its defined region. Unlike the original DeepAneDet model, which is optimized for detecting near-spherical lesions such as aneurysms, our method replaces the spherical target representation with a standard bounding box format (\(x\), \(y\), \(z\), _width_, _height_, _depth_), better accommodating bleeding regions with varying shapes. Figure 1: Overview of the proposed bleeding detection pipeline. CT images are preprocessed and passed through a 3D object detection model with an atrous spatial pyramid pooling (ASPP) module. False positives (FPs) are reduced using organ-overlap filtering, small box removal, and non-maximum suppression. Multi-organ segmentation supports this process and the 2D U-Net model was trained in our previous study [18]. The method achieves fewer false positives and faster detection. The input VOI was encoded into feature maps through the backbone. These feature maps were then processed by the detection head, which divided them into a grid of 1728 cells using two parallel convolution layers. The first convolution yielded a confidence score (_c_) for each cell, indicating the probability that a bleeding center was located within that cell. For positive cells (i.e., those containing a bleeding center), the second convolution estimated the center coordinates (\(x,y,z\)) and dimensions of the bounding box (_width_, _height_, _depth_). Activation functions were applied after the final 3D convolutions: a sigmoid function was used to predict the confidence scores and center coordinates, while an exponential function was applied to predict the bounding box dimensions. To further improve the detection performance, we incorporated an ASPP module [21] at the beginning of the detection head. The structure of the ASPP module used in our model is illustrated in Figure 3. This module enhances the model's ability to capture multiscale contextual information by applying atrous (dilated) convolutions with different dilation rates [21], making it effective for detecting bleeding regions of varying sizes and shapes. Based on preliminary experiments, we selected dilation rates of [3; 6; 9]. During the training phase, pairs of normalized images and their corresponding bleeding regions were used. For each bleeding region, a 3 \(\times\) 96 \(\times\) 96 \(\times\) 96 cubic VOI was extracted centered at the center of gravity of the bleeding region. Data augmentation involved the generation of four variants from each original VOI using random spatial transformations. These included shifts of up to 16 voxels along each spatial dimension and fixed-angle rotations (0\({}^{\circ}\), 90\({}^{\circ}\), 180\({}^{\circ}\), or 270\({}^{\circ}\)) in all anatomical planes (axial, coronal, and sagittal) to simulate positional variability. These augmentations were dynamically applied across epochs. The model was trained for 100 epochs with a batch size of four  
Figure 3: Structure of the ASPP module. Figure 2: Three-dimensional object detection model used in bleeding detection. using stochastic gradient descent (SGD) with a momentum of 0.9. A stepwise learning rate scheduler was applied, with rates set to 0.01 for epochs 1-50, 0.001 for epochs 51-75, and 0.0001 for epochs 76-100.